Outdoor

Seamless Pedestrian Navigation in Indoor/Outdoor Large Spaces with No Clear Patterns for Movement PhD Research Proposal

Jinjin YAN, MSc PhD candidate 2016 - 2020

December 1, 2017

Faculty of Architecture and the Built Environment

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Contents 1

Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Research outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Structure of this report . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Background 2.1 Spaces . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Indoor space & Outdoor space . . . . . . 2.1.2 Semi-indoor space & Semi-outdoor space 2.1.3 Two complete definition methods . . . . 2.2 Space-subdivision-based Navigation Network . 2.2.1 Poincaré Duality . . . . . . . . . . . . . . 2.2.2 2D approaches . . . . . . . . . . . . . . . 2.2.3 3D approaches . . . . . . . . . . . . . . . 2.3 Criteria in Space Subdivision . . . . . . . . . . .

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Proposed PhD Research 3.1 Problems in pedestrian navigation . . . . . . 3.2 Research questions . . . . . . . . . . . . . . . 3.3 Scope of the research . . . . . . . . . . . . . . 3.4 Methodology . . . . . . . . . . . . . . . . . . 3.4.1 Spaces . . . . . . . . . . . . . . . . . . 3.4.2 Spaces-subdivision . . . . . . . . . . . 3.4.3 Criteria . . . . . . . . . . . . . . . . . . 3.4.4 Implementations . . . . . . . . . . . . 3.5 Preliminary Results . . . . . . . . . . . . . . . 3.5.1 Pedestrian definition . . . . . . . . . . 3.5.2 Spaces classifications and definitions . 3.5.3 Spaces Management . . . . . . . . . . 3.5.4 Spaces Computation . . . . . . . . . .

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Practical Issues 43 4.1 Courses & Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2 Software, Tools and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5

Deliverables 5.1 Outcomes and Reports . 5.2 Time Planning . . . . . . 5.3 Short term first year plan 5.4 Publications . . . . . . .

Reference

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List of Figures 1.1

Examples of current problems in pedestrian navigation. . . . . . . . . .

2.1 2.2 2.3

Gas station. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Four indoor/outdoor environments (Wang et al., 2016). . . . . . . . . . 12 Three indoor/outdoor environment types and the representative scenes (Zhou et al., 2012). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Poincaré duality (Li, 2016). . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Navigation network derived using MAT (Kallmann and Kapadia, 2014). 14 Space partition according to Visibility Graph (Stoffel, Lorenz, and Ohlbach, 2007). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 A set of polygonal objects (a), the associated visibility graph (b), and a shortest path (c) (Berg et al., 2013). . . . . . . . . . . . . . . . . . . . . 15 Grid tessellation of indoor space (Afyouni, Cyril, and Christophe, 2012). 16 Adaptive extended Voronoi diagram (Hilsenbeck et al., 2014). . . . . . 16 Space partition based on irregular triangulations: CDT approach (Borovikov, 2011) and Constrained TIN (Xu, Wei, and Zlatanova, 2016). . . . . . . . 17 Representing and subdividing space by using cubes (voxels) (Yuan and Schneider, 2010) and Octree (Rodenberg, Verbree, and Zlatanova, 2016). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Example 3D volume based complete space subdivision in an indoor building (Boguslawski and Gold, 2009). . . . . . . . . . . . . . . . . . . 19 Space subdivision and navigation network based on convex polyhedron (Diakité and Zlatanova, 2017). . . . . . . . . . . . . . . . . . . . . . 19

2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16

Walls in volumetric-based space model. . . . . . . . . . . . . . . . . . . Wallsurface/‘paper’ wall in the CityGML (Gröger et al., 2008). . . . . . Example of the distance between people and wall in a straight corridor (Bosina et al., 2016). . . . . . . . . . . . . . . . . . . . . . . . . . . . Possible subdivision approach for indoor space (Diakité and Zlatanova, 2017). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Outdoor navigation network based on space subdivision (Rusné, Ljiljana, and Beirao, 2017). . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dimension of the pedestrian in 3D navigation network. . . . . . . . . . Information desk with functional areas (coloured areas). . . . . . . . . Workflow of my research. . . . . . . . . . . . . . . . . . . . . . . . . . . Planned experimental scenarios in this research. . . . . . . . . . . . . . Proposed definition and types of the pedestrians. . . . . . . . . . . . . The flow chart of defining the spaces. . . . . . . . . . . . . . . . . . . . Roof and top in the building. . . . . . . . . . . . . . . . . . . . . . . . . Our defnitions of the spaces. . . . . . . . . . . . . . . . . . . . . . . . . . Features of the spaces. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bridges between two buildings. . . . . . . . . . . . . . . . . . . . . . . . Some examples of the spaces. . . . . . . . . . . . . . . . . . . . . . . . .

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28 29 29 29 30 30 33 33 34 35 35 36 37 37 38 39

vi 3.17 The volumetric-based data model of spaces. . . . . . . . . . . . . . . . . 40 3.18 Experiment results of the imagery 3D bookstore. . . . . . . . . . . . . . 40 5.1

Time planning overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

vii

List of Abbreviations MAT CDT NRS NRG MLSM GPS LBS RFID TIN DG VG GNM 1D 2D 3D IPS INS OGC POI IFC BIM

Medial Axis Transform Constrained Delaunay Triangulations Node-Relation Structure Node-Relation Graph Multi-Layered Space Model Global Positioning System Location Based Services Radio Frequency Identification Triangulated Irregular Network Dual Graph Visibility Graph Geometric Network Model One Dimensional Two Dimensional Three Dimensional Indoor Positioning Systems Inertial Navigation System the Open Geospatial Consortium Point of Interest Industry Foundation Classes Building Information Modelling

1

Chapter 1

Introduction 1.1

Motivation

Navigation, also called path-finding or way-finding, whether in real or electronic world, is a fundamental but also complex activity. The navigation is described as “the method of determining the direction of a familiar goal across unfamiliar terrain” (May et al., 2003). Way-finding is the process of orientation and navigation in order to reach a specific distant destination from the origin especially in complex ¯ and spacious environments indoors or outdoors (Kruminait e˙ and Zlatanova, 2014). Way-finding is the process of orientation and navigation in order to reach a specific distant destination from the origin especially in complex and spacious indoors and outdoors environments. Pedestrian navigation is very important to us, because we have problems finding the ways at some places, e.g., in some public buildings such as airports, stations, and other. Seamless pedestrian navigation is one of the ultimate goals of the development of location based services (LBS) applications (Vanclooster and Maeyer, 2012). In the real world, outdoor space and indoor space do not exist in isolation and pedestrians move seamlessly from indoor to outdoor (Nagel et al., 2010). Therefore, indoor and outdoor seamless navigation is an inevitable trend and also an emergent task. It seems that over the last decade various seamless navigation systems have been developed, and the seamless is not an issue any more, for instance, GPS positioning plus Inertial Navigation System (INS) in (Cheng et al., 2014), GPS for outdoor positioning and QR codes for indoor positioning (Nikander et al., 2013) (Shelke et al., 2016), the system (Kourogi et al., 2006) based on GPS and active Radio Frequency Identification (RFID) tag, etc. Actually, the seamless pedestrian navigation is still not very successful yet. There are three reasons: (a) Only positioning technologies are unable to complete navigation, because successful accomplishment of navigation task involves: localisation of the start point and destination, route computation and guidance of the user (Worboys, 2011). Therefore, the integrated indoor and outdoor localisation technologies only have contributions on the localisation of the start point and destination, rather than complete seamless pedestrian navigation. (b) The consistent practice for pedestrian navigation in outdoor space is that obtaining pedestrian navigation network by reusing and adapting the road network based on pedestrian features. However, the predefined road network by strict regulations is originally designed for cars and other vehicles navigation purposes, rather than for pedestrians. (c) In reality, the pedestrians are special navigation objects, because of their speed (Corbetta et al., 2016) (Ziemer, Seyfried, and Schadschneider, 2016), dimension (Liu and Zlatanova, 2015), flexibility (Gaisbauer and Frank, 2008), behaviours (Hughes, 2002) (Hoogendoorn and Bovy, 2004) (Bosina et al., 2016) (Köster, Lehmberg, and Dietrich, 2016), etc. Specifically, there are five key differences between car traffic and pedestrian traffic, including Dimensionality,

2

Chapter 1. Introduction

E Door2 Path 1

Door1

Path 2

Path 3 S

(a) Separated pedestrian navigation

(b) Limited seamless navigation

E

(c) Network glued by anchor node

(d) Unrealistic detour in the square

(e) Passable entrance with bollards

(f) Guide pedestrians to walk in the street

F IGURE 1.1: Examples of current problems in pedestrian navigation.

Direction, Contact, Interaction, and Anisotropy. Specifically, Dimensionality means that the movement of the vehicles is in one dimension, while pedestrians move in two dimensions; Direction is that, in general, the vehicle traffic is a single direction flow, but pedestrian traffic is multiple directional flow; Contact is that there is no physical contact between vehicles during their movements, but pedestrians may have physical contacts; Interaction means that rules have strong effect on the interactions of vehicles, while the movement behaviours of pedestrians are driven by their subconscious; Anisotropy: vehicles and pedestrians have strongly anisotropy and mildly anisotropic respectively. Hence, compared with vehicle locomotions, pedestrians have great freedoms, such as they can walk freely in the passable spaces, where can hold their size. They can have different tracks, even with same origin and destination within the same spaces, especially in the spaces with no clear patterns (or paths) for pedestrian movements. We can call the spaces without clear patterns (or path) as “large spaces”. To some extent, all of the spaces in indoor and outdoor are large spaces from the pedestrian perspective. Moreover, these large spaces typically

1.1. Motivation

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contain areas that serve as attractors for pedestrians at different times during the day or under different circumstances (e.g. the coffee area or the corner of the bookstore), which increases the flexibility of navigation but also increases the difficulty of seamless indoor and outdoor navigation. Specifically, there are several aspects that have not been well solved in current indoor and outdoor seamless pedestrian navigation. • It is impossible for pedestrians to choose between indoor and outdoor paths. Currently, the pedestrians are not navigated seamlessly in indoor and outdoor, since current navigation for indoor and outdoor are separated. In other words, the navigation solutions are separated in two worlds, indoor world (space) and outdoor world (space). Actually, we are living in a continuous world (space), and the essence of the space in indoor and outdoor is the same, although the indoor environment is different from the outdoor environment. Figure 1.1(a) (Jensen et al., 2016) shows an example of current indoor and outdoor seamless navigation from the start point (S) to the destination (t), in which the shortest way is S ! M ! D ! t, rather than S ! A ! B ! C ! t. As users, to get the optimal path, they may have to install two different navigation applications, one for outdoor, and another for outdoor, unless they think that it is doesn’t matter always to make a detour around buildings. Without the collaborative applications, they cannot precompute a specific path including both indoor and outdoor at the same time. In other words, they need to switch the navigation application at M to get the path including the indoor space, otherwise they may have no choice to follow the only outdoor path. • In outdoor space, the navigation network mainly designed for car navigation purposes, and some research/applications try to reuse and adapt it for pedestrian navigation, then glue it to indoor navigation network by anchor node (Lee et al., 2014) and links (Teo and Cho, 2016). Figure 1.1(c) shows the glued indoor and outdoor navigation network by anchor. This approach can lead to two shortages for pedestrian navigation: the seamless pedestrian navigation is limited, and the navigable space in outdoor for pedestrian navigation has been shrunken or expanded. – Pedestrians not just want to get a general navigation path, but a path can meet their specific requirements. For instance, if a pedestrian forgets to take an umbrella, or sudden change of the weather, in a rainy day, he/she prefers to walk as much inside the buildings as possible, since the buildings can offer him/her the shelters can help him/her to escape from the rain. More examples like that large road with sidewalks, which are safer for pedestrians, may be preferable to the shortest route (Koide and Kato, 2005). Pedestrians like to walk on the road with street lights in the evening as the lights can give them a sense of security. Figure 1.1(b) illustrates navigation paths for pedestrians from the start point (S) to the destination (E), in which the Google Maps showed three paths. Paths 1 is a more suitable path than Path 2 and 3 in rainy day for the pedestrians who have no umbrellas, because it includes part of indoor path. However, pedestrians prefer to walk inside the building and go outside by the Door 1 or Door 2 to reduce the exposure in outdoor. These examples show that the current seamless indoor and outdoor pedestrian navigation is still very limited.

4

Chapter 1. Introduction – Navigable space in outdoor for pedestrians is shrunken, because the navigation network limits the flexibility of pedestrians. In other words, current navigation networks try to limit pedestrian walk where for pedestrians, e.g., cross the road by crossing the pavement, or make a detour when passing the space without clear paths. Actually, the pedestrian can ignore the pavements if they want. They can move as in indoor, considering the entire space available for walking. We know that compared to the cars, pedestrians have the freedoms to walk(except for some special areas), which means that they can walk freely to somewhere vehicles cannot, especially the pedestrian areas, for example, pedestrians can pass through the grass, even there are no clear paths, but vehicles cannot. The navigation paths given by Google Maps in Figure 1.1(d) from the start point (S) to the destination (E) only based on the road network, so they navigate pedestrians make a detour around the Delft square. But the reality is that pedestrians can walk through the square directly. What’s more, some place are physically blocked for cars, but pedestrians still can go through. Figure 1.1(e) shows an entrance with bollards, in which the entrance is only passable for pedestrians. Another situation is that the navigable space for pedestrians is expanded. For instance, the seamless navigation path guides pedestrians to walk in the street, which is not allowed in real life, see Figure 1.1(f). • Indoor navigation network construction based on space subdivision for pedestrians has been more investigated than outdoor, but fewer research is focused on the issue that to share the same subdivision rules from indoor to outdoor pedestrian navigation network construction. Outdoor is actually not much different that indoor as pedestrians are concerned. Therefore it is worth to investigate approaches that have been applied indoor for outdoor applications, rather than only adapt navigation network from the network originally for vehicles.

1.2

Research outcome

The goal of this research is to: A framework to construct 3D spatial seamless navigation model for pedestrian in indoor and outdoor, in which some contexts will be taken into account. To achieve this goal, this research will include defining spaces (indoor, outdoor, semi-indoor, and semi-outdoor), space management, determining objects based on the pedestrian contexts in subdivision, 3D space subdivision based on variable size cells, navigation network construction, and paths computation (for tests).

1.3

Structure of this report

This document comprises five chapters. • The current chapter introduces the motivation, the research outcome, and the scope of the research. The remaining chapters are as follows:

1.3. Structure of this report

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• Chapter 2 summaries background for this research. Several essential concepts of indoor and outdoor navigation are introduced, including spaces, spacesubdivision-based navigation network, and the contexts in pedestrian navigation. • Chapter 3 is used to describe problems in current pedestrian navigation, research questions, methodology, and some preliminary results, in which we refine one main research question with three sub-questions. • Chapter 4 involves some specific practical issues, including courses, supervision, software, tools and data. • Chapter 5 indicates the proposed deliverables of this research, and also the time planning.

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Chapter 2

Background There are a variety of research focused on indoor and outdoor pedestrian navigation, including spaces and space modelling (Becker, Nagel, and Kolbe, 2009a) (Lee ¯ et al., 2014) (Boguslawski and Gold, 2009), space subdivision (Kruminait e˙ and Zlatanova, 2014) (Zlatanova et al., 2014) (Diakité and Zlatanova, 2017), navigation network models (Liu and Zlatanova, 2011) (Mandloi and Thill, 2010) (Teo and Cho, 2016), context in navigation (Afyouni, Ray, and Claramunt, 2010) (Afyouni, Cyril, and Christophe, 2012) (Becker, Nagel, and Kolbe, 2009b) (Khan, Yao, and Kolbe, 2015) (Saeedi, 2013) (Wu et al., 2007), and indoor and outdoor seamless navigation (Basiri et al., 2016) (Cheng et al., 2014). In this chapter, we will focus on three main points from the pedestrian navigation perspective in current research: spaces, spaces-subdivision, and the context-aware, in which, the spaces include the space definition, space management, and space modelling, the space-subdivision includes indoor space subdivision rules, and navigation network construction, and the context-aware is only the context related to the space subdivision.

2.1

Spaces

We are living in spaces. Although there are some different features in different spaces, e.g., constraints (Li, 2008) (Yan et al., 2016), positioning technologies (Yang and Worboys, 2011b), etc, the essence of the spaces in indoor and outdoor is the same from the point of view of pedestrians, because only free (unoccupied) spaces pedestrians can walk in, and they can only navigate through spaces where they are allowed to go. People can walk in both indoor and outdoor (Yang and Worboys, 2011a), during this they inevitably access to some spaces, where can be called semiindoor space or semi-outdoor space, because they have some features of both indoor space and outdoor space, e.g. the space under an overpass, bus shelters, porch, etc. From the perspective of pedestrian navigation, to offer proper services to pedestrians, it is necessary to define the spaces clearly, because (a) Pedestrians have the greatest freedom. Because of the greatest freedom of pedestrians and the space in outdoor is open rather than enclosed like the indoor space, pedestrians may move in some spaces, where for other locomotions only or not accessible theoretically. For example, pedestrians can across the lane, even though there is no crosswalk lines. Another example is that grass is not accessible theoretically for vehicles and pedestrians in normal situation, but pedestrians can across it without any barriers. What’s more, not all of the passable space is walkable for pedestrians, some of them for cars only (high ways), bikes only (bicycle lanes), etc., which means that we need to find the walkable space for pedestrians and enclose the outdoor space for them. As it will be very useless to navigate them without closing the space, e.g., navigate them to

8

Chapter 2. Background

through high way, pass a river without a bridge, etc. (b) Pedestrians are interested in different spaces. For instance, people prefer to stay in indoor space in harsh weather, while they would like to have some outdoor activities in nice weather. When it rains, pedestrians need more space with shelters that can help them to escape from the rain (Koide and Kato, 2005). Moreover, the semi-indoor/outdoor spaces play important and non-negligible roles, since they can act as connections in the indoor and outdoor space, improve residential comfort and reduce cooling and heating energy requirements(Kim, Kim, and Leigh, 2011), and they are very important in the spatial design to obtain a good building microclimate (Du, Bokel, and Dobbelsteen, 2014). Moreover, because of a better thermal sensation (usually lower effect of wind, less hot) than the outdoor space inside the semi-outdoor space (partially controlled space)(Pagliarini and Rainieri, 2011b), people prefer to use them for some purposes, e.g., drying laundry and growing plants (Kim, Kim, and Leigh, 2011), learning activities (Nasir, Salim, and Yaman, 2014), adapting to thermal environments (Lin, 2009), sheltering from the sun and wind (He and Hoyano, 2010), improving the physical environment of the markets(Kim, Park, and Kim, 2008), etc. (c) The applications are different in different spaces. Only knowing that what space is when pedestrians are in a real environment (space), we can decide when and what applications should be switched on. For example, we can rely on the GPS in the outdoor space and part of indoor space, but we need to switch on the indoor positioning technologies when pedestrians move into the indoor space, since the GPS cannot work well or even become invalid in indoor, because of the signal attenuation and multi-path (Lachapelle, 2004). Therefore, clear classifications, definitions, as well as the features of the spaces for pedestrian navigation are critical. However, even what space is when pedestrians are in a real environment (space) have not reached an agreement, in other words, there are no universally recognised definitions of spaces in current research, especially the semi-indoor or semi-outdoor spaces. The common definition of indoor space is a space (building) bounded by the wall, floor and roof (Lee et al., 2014) (Afyouni, Ray, and Claramunt, 2010), while the outdoor space is the space out of building (indoor space), or in the open air (Wang et al., 2016) (Zhou et al., 2012). There are four kinds definitions of the semi-indoor or semi-outdoor space, which are mainly described as follows: some of them describe it as outdoor (Van Timmeren and Turrin, 2009), since they think this space is not enclosed completely by walls, windows, doors, etc.; some researchers regard it as semi-indoor (Turrin et al., 2009) (Kim, Kim, and Leigh, 2011) (Chengappa et al., 2007) (Tyson, Schuler, and Leonhardt, 2012) (Amutha and Nanmaran, 2014) (Liu et al., 2013) (Kim, Park, and Kim, 2008) (Monteiro and Alucci, 2007), because it is covered with canopies, is related to the buildings, and can combine indoor and outdoor climate; Some researchers take it as semi-outdoor (Du, Bokel, and Dobbelsteen, 2014) (Hwang and Lin, 2007) (Lin et al., 2008) (Indraganti, 2010) (Pagliarini and Rainieri, 2011b) or semi-enclosed space (He and Hoyano, 2010) (Kim, Kato, and Murakami, 2001), since it is not enclosed completely, and has some settings included man-made structures that moderate the effects of the outdoor conditions; Some researchers regard it as connection/transition/buffer areas between indoor and outdoor (Slingsby and Raper, 2008) (Li, 1994). The main reason for these different views is that the definition of space is not clear. The spaces in the world can be divided and named based on three criteria. • based on the structure:

(a) (Amutha and Nanmaran, 2014) (Shooshtarian and Ridley, 2017), Space = Indoor space

S

Outdoor space

S

Semi

indoor space,

2.1. Spaces

9

(b) (Du, Bokel, and Dobbelsteen, 2014) (Yang, Wong, and Jusuf, 2013) (Lin et al., 2008) (Hwang and Lin, 2007) (Wang et al., 2016) (Zhou et al., 2012) Space = Indoor space

S

Outdoor space

• based on the navigation:

S

Semi

outdoor space;

Space = N avigable space (Slingsby and Raper, 2008) (Becker, Nagel, and Kolbe, 2009b);

S

N on

navigable space

• based on the space subdivision (Zlatanova, Liu, and Sithole, 2013) (Diakité and Zlatanova, 2017): Space = Object space

S

F unctional space

S

F reespace.

In this research, we only focus on the space based on the structure, but four kinds of spaces are included, i.e., indoor space, outdoor space, semi-indoor space, and semi-outdoor space.

2.1.1

Indoor space & Outdoor space

The indoor space is a space within one or multiple buildings consisting of architectural components in IndoorGML (Lee et al., 2014). (Afyouni, Ray, and Claramunt, 2010) defined the indoor space as a building environment (such as a house, a commercial shopping center, etc.) that people usually behave. Indoor space in (Yang and Worboys, 2011a) refers to built rather than natural environments. It covers the enclosed interiors of buildings above the ground and spaces underneath the ground that afford platforms for human activities. (Winter, 2012) defined the indoor space by analogy with human body. The body is a container enclosed by skin, because of the skin, the body becomes isolated from the outside, so the skin brings the concept of inside and outside to the body. Similarly, the wall, floor, roof, fence, etc. are skin, which bring the concept of inside and outside to the space. Then, inside of the skin is indoor space, and outside the skin is outdoor space. The doors are entrances. Indoor spaces in (Zlatanova et al., 2014) are artificial constructs designed and developed to support human activities. Virtual representations of indoor spaces have to be able to support these activities. What’s more, indoor and semi-indoor also can be defined as GPS-denied scenarios (Ortiz et al., 2015). It seems that there is no formal definition of the outdoor space, or we can take the outdoor space as the space out of buildings or the space where GPS is available. The current definitions of indoor space and outdoor space from dictionaries (Table 2.1) and literatures are related to the building or house, i.e., the space inside or interior of the house or building is indoor space, while the space out of the building, out of doors, in the open air, or wildness is outdoor space. These definitions are reasonable to a certain extent, but they did not include the semi-enclosed space, for example, the gas station, see Figure 2.1, which cannot be categorised as indoor (not inside a building), nor outdoor (not in the open air).

2.1.2

Semi-indoor space & Semi-outdoor space

The definition of semi-indoor space is quite related to the roof, because it can make a big difference on the climate of the space. The roof is expected to play a key role in contributing to the climate control . Then, (Turrin et al., 2009) defined the covered space as semi-indoor space, which are partially surrounded by adjacent indoor spaces. (Van Timmeren and Turrin, 2009) covered the space by the Vela Roof to create

Chapter 2. Background 10

Sources Dictio-

Indoor

Outdoor

TABLE 2.1: The definitions of Space, Indoor, and Outdoor from different resources. Space

Indoor means happening, used, or existing inside a building, e.g., indoor sports/activities, an indoor racetrack/swimming pool.

Indoor means occurring, used, etc., in a house or building, rather than out of doors, e.g., indoor games.

Outdoor means taking place, existing, or intended for use in the open air, e.g., outdoor games, outdoor clothes. Also out-of-door; out of doors; in the open air; the world outside of or away from houses.

Indoor means of, situated in, or appropriate to the inside of a house or other building; Or of the inside of a house or building; Or living, belonging, or carried on within a house or building. Indoor is of, situated in, or appropriate to the inside of a house or other building, e.g., an indoor tennis court; indoor amusements.

Outdoor means the open air. Or an area away from human settlements.

Outdoor means existing, happening, or done outside, rather than inside a building, e.g., an outdoor swimming pool/festival, outdoor clothes; Or liking/relating to outdoor activities, such as walking and climbing. Outdoor, also out-of-door, means taking place, existing, or intended for use in the open air, e.g., outdoor games, outdoor clothes.

Outdoor is a done, situated, or used out of doors.

Space is a continuous area or expanse which is free, available, or unoccupied; An area of land which is not occupied by buildings.

Outdoor is of or relating to the outdoors; not enclosed: having no roof. done, used, or located outside a building.

Oxford narya

Indoor is a situated, conducted, or used within a building or under cover. The origin of indoor is from in (as a preposition) + door in early 18th century, superseding earlier within-door. Indoor is of or relating to the interior of a building; done, living, located, or used inside a building.

MerriamWebsterb

Cambridge Dictionaryd

Dictionaryc

Space is a boundless three-dimensional extent in which objects and events occur and have relative position and direction, e.g., infinite space and time. Or a physical space independent of what occupies is called also absolute space. The space is the unlimited or incalculably great threedimensional realm or expanse in which all material objects are located and all events occur. It also can be defined as the portion or extent of this in a given instance; extent or room in three dimensions, for instance, the space occupied by a body. The space is an empty area that is available to be used. From the the dimension of view, the space is the area around everything that exists, continuing in all directions.

Collins Dictio- The space refer to an area that is empty or available, narye and the area can be any size. Space means the unlimited three-dimensional expanse in which all material objects are located. Space is the whole area within which everything exists. TheFreeDictionaryf The space is an infinite extension of the threedimensional region in which all matter exists.

a https://en.oxforddictionaries.com b https://www.merriam-webster.com/dictionary/ c http://www.dictionary.com/ d http://dictionary.cambridge.org https://www.collinsdictionary.com/ http://www.thefreedictionary.com/ f

e

2.1. Spaces

11

F IGURE 2.1: Gas station.

semi-indoor space to passively avoid uncomfortable conditions (coldest and overheated) in this. Thereby further reducing relevantly the energy demand for thermal comfort. Therefore, in their case, the space covered by roof but not an indoor space is semi-indoor space. (Hooff and Blocken, 2009) clearly defined the semi-indoor space in the research titled computational analysis of natural ventilation in a large semienclosed stadium. Specifically, by closing the roof, a semi-indoor environment is created and spectators and equipment are protected from wind, rain and snow. The example is semi-indoor stadia, which are characterised as stadia that have a roof that can be used to close the indoor volume to a relatively large extent, but that even in this case still have direct openings to the outside. However, (Bouyer et al., 2007) took the stadium as the semi-outdoor space. The definition of semi-outdoor space is given in many literatures, especially from the assessment thermal comforts aspect, and the reason why most of them defined the space as semi-outdoor space is that these spaces have the roof and partly open to the outdoor environments. (Pagliarini and Rainieri, 2011b) regarded the space enclosed by a semi-transparent pitched roof is semi-outdoor space. (Spagnolo and De Dear, 2003) defined the semi-outdoor as locations that, “while still being exposed to the outdoor environment in most respects, include man-made structures that moderate the effects of the outdoor conditions.” Examples in it include roofs acting as radiation shields or walls acting as vertical windbreaks. The term semi-outdoor in (Pagliarini and Rainieri, 2011a) refers to a space which is partially open towards the outdoor environment. A specific group of semi-outdoor spaces in (Turrin et al., 2012) can be identified as covered by large roofs or partially open envelopes, leaving a relevant direct connection with the outdoor environment. Museums, cultural centres, university campuses, shopping and leisure areas, hotels and resorts, are just a few examples of built environments where covered semi-outdoor spaces are commonly integrated. (Lin, Matzarakis, and Huang, 2006) took the bus shelters as example of semi-outdoor space, as the space has shelter provided in the form of a roof. The semi-outdoor space in (Yang, Wong, and Jusuf, 2013) refers to the internal architectural space in a given building with maximal exposure to the outdoor climate such as lobbies, corridors, atriums, courtyards, passages, verandas, etc. In the (Goshayeshi et al., 2013), semi-outdoor spaces can be defined as the spaces which are partly open in the direction of the outdoor circumstance, in which three categorisations are introduced: the first type is located inside the buildings such as entry atrium. The second type is covered spaces which are connected to structure like balcony. The last type is shaded spaces situated in outdoor environment entirely. Station, covered street and pavilions are regarded in this category. (Hwang and Lin, 2007) defined the semi-outdoors as ‘exterior spaces that are sheltered and attached to the building’.

12


In summary, above literatures parsed and defined the semi-indoor space and semi-outdoor space from multiple perspectives. While these arguments appear to make sense, the definition still varies from person to person, from application to application, even for the same space, for instance, the stadium.

2.1.3

Two complete definition methods

According to the number of available satellites for positioning, (Wang et al., 2016) defined the space (Figure 2.2) into four categorises, Open Outdoors, Semi-Outdoors, Light Indoors, and Deep Indoors. The area, where has the open sky condition, and people can have access to enough satellites for positioning, is Open Outdoors. The area, a GNSS-hostile outdoor environment and without enough satellites for positioning, such as an urban canyon or a wooded area, is Semi-outdoors. Light indoors is similar to semi-outdoors, but still some satellites are available by the windows, while the Deep indoors environment refers to a place without any available satellites at all.

F IGURE 2.2: Four indoor/outdoor environments (Wang et al., 2016).

To provide fine-grained context information for a generic service for indoor outdoor detection, (Zhou et al., 2012) classified the environment into three categories (i.e. outdoor, semi-outdoor, and indoor). To be specific, the space outside a building is outdoor, while inside a building is indoor. Close to or semi-open building space is semi-outdoor. Figure 2.3 illustrates the definitions, examples and representative scenes for those three different spaces.

F IGURE 2.3: Three indoor/outdoor environment types and the representative scenes (Zhou et al., 2012).

The above two complete definition methods based on different criteria and applications, but they are the same in essence, i.e., the spaces are categorised into three

2.2. Space-subdivision-based Navigation Network

13

types, indoor, outdoor, and semi-outdoor. However, there are some scenes beyond above definitions, for example, the gas station. For the first method based on number of available satellites, the gas station is not an outdoor space, because of the roof; nor a semi-outdoor space, because it is not near a building and entirely possible to get enough satellites for positioning; nor a light indoor, because it is not a room with windows; it is completely impossible to be deep indoor. For the second method, not outdoor, not indoor, and also not semi-outdoor.

2.2

Space-subdivision-based Navigation Network

Currently, Indoor navigation network for pedestrians has been more investigated than outdoor, in other words, there are a variety of approaches for indoor pedestrian navigation network construction, but less for outdoor space. Grid-based navigation model, and network-based navigation model are two kinds of navigation models. In this research, we will focus on the more promising network-based navigation model, because it is the most common navigation model used for human navigation. Nodes in the network indicate landmarks or decision points and edges between them are the connections. Moving from one node to the other is allowed only when there is an edge between them. Usually cost of edges indicates distance or travel time between nodes (Mortari et al., 2014). Moreover, nodes can contain semantic information about the location (name, type, description, etc.). Compared to grid-based navigation model, the network-based navigation network model enables lower data processing time which is essential in large scenes both indoor and ¯ ˙ 2014). Furthermore, it tends to be used for human navioutdoor space (Kruminait e, gation and as such, and provides less detailed routing assuming human intelligence will compensate for inaccuracies (Zlatanova et al., 2014). Largely only geometry of the spaces is involved in current network for navigation, and existing methods include 2D approaches and 3D approaches (Zlatanova et al., 2014). Although these methods have the same goals or results to construct the navigation network, they have different strategies, as well as different criteria. Examples of the 2D approaches includes grid tessellation (Afyouni, Cyril, and Christophe, 2012), Medial Axis Transformation (MAT) (Kallmann and Kapadia, 2014), Voronoi diagrams (Hilsenbeck et al., 2014), Visibility Graph (VG) (Stoffel, Lorenz, and Ohlbach, 2007), Constrained Delaunay triangulation (CDT) (Borovikov, 2011) or constrained Triangulated Irregular Network (TIN) (Xu, Wei, and Zlatanova, 2016). Voxels (Yuan and Schneider, 2010) (Nourian et al., 2016), Octree (Fichtner, 2016) (Rodenberg, Verbree, and Zlatanova, 2016), and Convex polyhedron (Diakité and Zlatanova, 2017) are some examples of 3D approaches. More details about the approaches for network-based navigation models reconstruction can be seen in the following subsections.

2.2.1

Poincaré Duality

Navigation network construction based on space subdivision utilises the Poincaré Duality (Becker, Nagel, and Kolbe, 2009a) to simplify the complex spatial relationships between 3D objects by a combinatorial topological network model, i.e., a kdimensional object in N-dimensional primal space is mapped to a (N-k)-dimensional object in dual space. More specifically, solid 3D objects in primal space, e.g., rooms within a building, are mapped to vertices (0D) in dual space. The common 2D face shared by two solid objects is transformed into an edge (1D) linking two vertices

14


F IGURE 2.4: Poincaré duality (Li, 2016).

F IGURE 2.5: Navigation network derived using MAT (Kallmann and Kapadia, 2014).

in dual space. Thus, edges of the dual graph represent adjacency and connectivity relationships which may correspond to doors, windows, or hatches between rooms in primal space. Figure 2.4 illustrates this duality transformation.

2.2.2

2D approaches

Floor plans are common original inputs for 2D approaches for indoor/outdoor space subdivision to construct navigation network. In general, 2D approaches can be categorised into two types: (a) No subdivision or partial subdivision, i.e., drive a network for path computation directly, e.g., MAT, VG; (b) Complete subdivision, which includes regular tessellations (e.g., grid tessellation), and irregular tessellations (e.g., VG, CDT, constrained TIN). Then the navigation nodes are derived from the subspaces based on the Poincaré duality. MAT algorithm is a common approach to derive navigation networks, in which no subdivisions are needed. Essentially MAT is a thinning algorithm which provides the skeleton of the polygon (Figure 2.5). This approach is totally relied on the shape of the space, and it provides sufficient navigation path for regular spaces with long polygons but is not very appropriate for large open spaces, because the generated network might suggest navigation paths that are unrealistic and usually are not taken by people (Kallmann and Kapadia, 2014). Another common approach for navigation network construction is VG. (Stoffel, Lorenz, and Ohlbach, 2007) developed navigation model where the indoor space is partitioned into convex polygons according to the visibility criterion, see Figure 2.6. In contracts to MAT the VG does not rely on the shape of the building spaces


(a)

15

(b)

F IGURE 2.6: Space partition according to Visibility Graph (Stoffel, Lorenz, and Ohlbach, 2007).

(a)

(b)

(c)

F IGURE 2.7: A set of polygonal objects (a), the associated visibility graph (b), and a shortest path (c) (Berg et al., 2013).

totally, but provides a direct path to the point of interest. path connects the begin and end point directly, therefore, space subdivision is also not needed. A VG is a graph of intervisible locations, typically for a set of points and obstacles in the Euclidean plane. Each node in the graph represents a point location, and each edge represents a visible connection between them. That is, if the line segment connecting two locations does not pass through any obstacle, an edge is drawn between them in the graph. It can provide more realistic navigation paths as the length of the paths are optimal, see Figure 2.7. However, because the VG also connects corners of objects within indoor environment, the derived navigation path may suggest for the user of the navigation system to move too close to the walls or corners of objects within indoor space, which is contrary to the walking habits of pedestrian, because people always keep a certain distance to wall or corners of objects in a straight corridor (Bosina et al., 2016). What is more, if there are many obstacles with super complex shapes in environments, the visibility graphs are constructed with a large number of nodes and edges. Thus, a great amounts of storage space and time consuming calculations of navigation network and paths might become required. Grid tessellation is a well known subdivision of space. The space is subdivided into a finite number of non-overlapping areas and various types of information are assigned to the cells (Afyouni, Cyril, and Christophe, 2012) to represent navigable and impassable regions in space by associating different cell states, see Figure 2.8. A graph can also be generated from the grid: the nodes represent the centres of the grid cells and edges of a certain node represent link relationships between the node and its neighbours. This method includes regular tessellations and irregular tessellations, in which regular tessellations decompose space into cells that have the exact

16


(a) Square shaped grid

(b) Hexagonal grid

F IGURE 2.8: Grid tessellation of indoor space (Afyouni, Cyril, and Christophe, 2012).

F IGURE 2.9: Adaptive extended Voronoi diagram (Hilsenbeck et al., 2014).

same shape and size, e.g., primarily square- (Figure 2.8a) and hexagonal-(Figure 2.8b) shaped cells (Galˇcík and Opiela, 2016), while the irregular tessellations aim at providing an adaptive decomposition of space that is suitable to exactly represent the complexity of the environment being studied (e.g., to accurately represent obstacles). This approach commonly used in game industry to navigate agents as the agent does not have to follow strict guidelines and can freely move in space. However, the performance depends on the size of the model and size of the grid cell. Too coarse grid might be lost important information while overly fine grid might disproportionally increase processing time and consume excessive amounts of memory although it provides precise movement in space. Voronoi diagram1 or even adaptive extended Voronoi diagram in (Hilsenbeck et al., 2014) is also an excellent choice for partitioning (triangulation) of large space for 1

https://en.wikipedia.org/wiki/Voronoi_diagram


(a) CDT approach

17

(b) Constrained TIN

F IGURE 2.10: Space partition based on irregular triangulations: CDT approach (Borovikov, 2011) and Constrained TIN (Xu, Wei, and Zlatanova, 2016).

navigation, see Figure 2.9. The figure is an adaptive extended Voronoi diagram extracted from a floorplan. The left is a two-dimensional floorplan and its skeleton and the right is the final graph after sampling the skeleton (one-dimensionally) and enclosed free space (two-dimensionally). The structure of a building typically not only imposes hard constraints on where a pedestrian can walk (walls). It also outlines important priors where people are likely to walk or are not allowed to walk. Narrow spaces, for instance, are usually traversed from one end to the other. In contrast to robotic applications, sub-meter accuracy is not required for pedestrian navigation systems. Hence, this way, narrow spaces become one-dimensional, while open areas remain two-dimensional. However, such space representation is not suitable for accurate guidance. CDT is one of the most common approaches used to design navigation network (Figure 2.10a). This approach has two advantages: on one hand, the computations is fast and the network is easy to maintain, because the triangles are one of the simplest features for computers to processes, and it enforces obstacle constraints as specific segments are included in the triangulation process. On the other hand, the CDT allows determination of several constraints in order to derive the most suitable triangulation for generation of navigation network (Borovikov, 2011). Such constraints might be maximum area of triangle, minimum angle within triangle or maximum number of Steiner points that can be inserted. CDT is suitable for large open spaces as it can provide different movement options. Vertices of triangles, centroids of triangles or centres of triangle edges can be used as nodes in the navigation network. However, navigation network generated using triangulation typically lack for accuracy guidance and might provide navigation path with unrealistic turns (Afyouni, Cyril, and Christophe, 2012). Constrained TIN is another common irregular triangulations based approach, who is quite similar to the CDT approach. Therefore, this approach has similar advantages and disadvantages with CDT. Figure 2.10b shows the space subdivision results by the constrained TIN, in which the D1 and D2 are original point and destination separately. The O1, O2 and O3 are three simplified obstacles with different shapes, and the spaces surrounded by pink lines are subspaces. Then each triangle is represented by a point which becomes a node in the network for navigation. In some cases, a certain feature point (table, chair, bed, plant) can be found to represent the area, wherever inside the triangle.

18


(a) Voxels

(b) Octree

F IGURE 2.11: Representing and subdividing space by using cubes (voxels) (Yuan and Schneider, 2010) and Octree (Rodenberg, Verbree, and Zlatanova, 2016).

2.2.3

3D approaches

The 3D approaches are try to keep the three-dimensional properties of the space, and use the volumes to represent the sub-spaces, in which the volumes can be different shapes and sizes, such like cubes (voxels), octree nodes, polyhedron (concave or convex), tetrahedron, etc. With the sub-spaces and Poincaré duality theory, navigation network can be derived. A quite special approach to 3D space (especially with regular buildings) is that 2D navigation network will be embedded in 3D space into 3D navigation network, especially regular buildings (Zlatanova et al., 2014), which is actually not a 3D approach. The familiar methods of real 3D approaches are Voxels (Yuan and Schneider, 2010) (Nourian et al., 2016), Octree (Fichtner, 2016) (Rodenberg, Verbree, and Zlatanova, 2016), Convex polyhedron (Diakité and Zlatanova, 2017), etc. Representing and subdividing space by using cubes (voxels) is one of the most popular grid-based models in 3D approaches. In this model, the space is decomposed into cells with same size marked as obstacle or non-obstacle. Based on this representation, routes can be computed by checking the availability of cell movements to their eight neighbours. This model also supports navigation in the 3D space by filling out the indoor space with the obstacle and non-obstacle cubes (Figure 2.11a). The obstacle cubes are further classified into insurmountable and surmountable ones to facilitate the 3D navigation. However, this approach also depend on the size of the unit cubes (voxels). If the voxels is too coarse, important information or space might be lost, while fine voxels can increase the time for processing and need more memory to store, which could be a disaster in the outdoor space. An octree2 is a tree data structure in which each internal node has exactly eight children. Octrees are most often used to partition a three-dimensional space by recursively subdividing it into eight octants. Octrees are the three-dimensional analog of quadtrees. The name is formed from oct + tree, but note that it is normally written "octree" with only one "t". Octrees are often used in 3D graphics and 3D game engines. (Rodenberg, Verbree, and Zlatanova, 2016) employed the octree data structure to structure and segment a indoor point cloud for indoor path finding. An octree consist of a cubical volume which is recursively subdivided into eight congruent disjoint cubes (called octants) until blocks of a uniform colour are obtained, or a predetermined level of decomposition is reached. Compared to the voxels, Octree is 2

https://en.wikipedia.org/wiki/Octree


19

F IGURE 2.12: Example 3D volume based complete space subdivision in an indoor building (Boguslawski and Gold, 2009).

(a)

(b)

F IGURE 2.13: Space subdivision and navigation network based on convex polyhedron (Diakité and Zlatanova, 2017).

a an upgraded version, since it can represent the point and pointless space by volumes with different size. Moreover, partitioning of space by the octree can result in a hierarchical tree structure, which make operations of path finding like neighbour finding. Figure 2.11b shows the octree nodes (Rodenberg, Verbree, and Zlatanova, 2016) to represent the regular indoor sub-spaces, and each space can be a navigation node. 3D volume based complete space subdivision is also a common approach to subdivide the space for navigation network construction. Figure 2.12 illustrates the space subdivision processes and its navigation network, in which S1 and S7 are staircase, S2 S5, S8 S11 are rooms, S6 and S12 are corridor. a) spatial schema, b) volumetric model of rooms, c) complete graph of connections between rooms, d) graph of connections between rooms - only passages. This method can describe the space connections, but cannot derive detailed navigation network for accurate pedestrian navigation, because on the one hand, it abstracts a room as a single node within the network, rather than considering a detailed partition of a room space into different areas, which means that this method assumes that the indoor space is

20


empty (no obstacles), and all the space is navigable space for pedestrians. Actually, it is too coarse for pedestrian navigation. In addition above approaches, the space subdivision based on polyhedrons is a general method, since the polyhedron could be limited to concave polyhedron, convex polyhedron, polyhedron with same size, etc. But the convex polyhedron is the most popular one as the convex can guarantee the navigation nodes (centroids) located inside of the polyhedrons (sub-spaces) when using the Poincaré duality. Figure 2.13 shows an example of space subdivision and navigation network based on convex polyhedron, in which (a) is the sub-spaces of the navigable spaces for pedestrians and (b) is the 3D avigation network.

2.3

Criteria in Space Subdivision

In order to enable generation of more realistic navigation path, the objects in space subdivision are cannot be ignored, since these objects can act as obstacles, destinations, or dynamic factors in the path computations. Currently, most studies in space subdivision assume that the spaces are empty, or only a few obstacles based on the preferences of the researchers are included. Actually, different kinds of pedestrians have different purposes (in this research, we call them as contexts), thus, the navigation networks should include different objects (nodes, or sub-spaces) interesting for different kinds of pedestrians. For instance, tourists are interested in the attractions, where they can take good pictures, where they can find benches to have a rest, and where they can have a picnic, in which the best camera points, benches, and open space can be used for picnic are interesting objects (spaces) for them, but these objects are not interesting for pedestrians who want to walk with the dog for leisure only. Since the objects are heavily relied on pedestrians, we can analyse them from the types of the pedestrians. There are five reasons3 why people travelling as pedestrians, so there are five kinds of pedestrians at least: • Utilitarian Walking - People walk to destinations such as work, school or shopping areas. Most auto and transit trips include utilitarian walking to reach the final destination. • Rambling - People ramble as a recreational activity, typically for exercise or enjoyment. Rambling may include walking the dog, pushing a baby carriage, jogging, or walking briskly for exercise. • Strolling/Lingering - In certain settings, people stroll and linger. They may stand on the sidewalk and talk with others they meet, sit on a bench, or peoplewatch during an outing. • Promenade - People walk to be seen and interact with other members of the community (e.g. high school students who promenade in groups in shopping malls). • Special Events - People walk at farmer’s markets, public concerts, parades, arts festivals and other community events. 3 http://www.greatstreetsstlouis.net/residential-neighborhood-design/multimodal-corridorplanning/pedestrians

2.3. Criteria in Space Subdivision

21

What’s more, not only pedestrian types, but also the pedestrian route choices decide the context in pedestrian navigation. For instance, considering the pollution and noise levels as a factor, the spaces with some equipments who can make noise must be included in space subdivision to help pedestrians to avoided them. In conclusion, there are seven different types (Liu and Zlatanova, 2013) paths based on different purpose in navigation. • Shortest-distance: it is a path providing the minimum distance between origin and destination. This type of path is the most common one in our lives, because the shortest way can help us reduce the consumption of energy. But it also has a drawback that the shortest path isn’t the one that is passable or easy to be followed sometimes. Pedestrians seldom aware that they are minimising distance as a primary strategy in route choice, but they actually try to choose the shortest route. • Shortest time: the shortest time path is similar to the shortest distance way to some extent. However, this path has to take the speed into account. For example, someone in a hurry to somewhere, the need of he/she is not to find the shortest distance path, but the shortest time path. So, this navigation path should pay attention to the traffic condition, even though there will be a detour, but it can save time. • Simplest path: it means the path with minimum turns, because some pedestrians have weak direction and always get lost after several turns. But it could be longer distance and longer travel time since the simplest path has a detour to simplify the path. • Least-space-visited: this type of path can help pedestrian go through a building with the least-number of passed room, because the doors of rooms are not always open, and they may disturb the people who are working in these rooms. But this type of path also has the problem that it could be longer distance and longer travel time since the simplest path has a detour. • Most-space-visited: this type of path is helping some to visit as many as space as possible. For example, someone has to walk to somewhere in the poor weather conditions, e.g. raining day, he/she has no rain shelter, so the most suitable navigation path is most-space-visited path to find as many as shelters. Another example, someone is on travelling, he/she do want to travel more interesting places with the limited time. But it also could be longer distance and longer travel time. • Least-obstruction: it is a path considering the least degree of blockage between origin and destination, but gaining an accurate path of such type needs accurate dynamic information. The information is difficult to be exactly collected in practice. • Safest path: this type of path is try to avoid some specific areas for the sake of safety, even if a detour is required. For example, someone has to go to somewhere in evening, he/she wants to find the path equipped with street lights, and less fast cars. • Specific level of calories burn: this path is specifically for people who want to burn their calories by exercises, e.g., walking, running, jogging, etc. For

22

Chapter 2. Background example, someone wants to find the way to somewhere and burn calories to lose weight at the same time. • Minimum traffic related air pollution exposure: some people are sensitive to the air with automobile exhaust, because of some respiratory problems. Therefore, they need to find a way or place, where has less car exhaust emissions. • Minimum slope variation: people prefer to roads with minimum slope variation, for example, they need to change dwelling place, and transfer something by manpower. So, the road is more flat the better. TABLE 2.2:

Examples of criteria in indoor spaces subdivision ¯ (Kruminait e˙ and Zlatanova, 2014).

Criteria

Influence

Examples

Attractiveness of spatial element

People distribution within an area

Shop windows or artworks in galleries VS workplaces or printing corners

Necessity of spatial element

Functional areas

Information desks VS shops and cafeterias in stations

Object’s closeness to central point of the environment


Restaurants, cafeterias or shops in airports which are far away from gates, because people want the least efforts to continue their trip

Limited capacity

Contain a limited number of people

The benches in waiting halls of airports also can be seated by a limited number of people

Transition zone


To see a painting or an information screen, people stand at a certain distance from these objects

Private space

The size of the functional area of an object

Co-workers in office VS travellers in in railway stations

More importantly, in addition to the object itself, their functional space also has a significant impact on space subdivision, because the functional areas are not be ¯ occupied physically, but they are not passable. Functional areas in (Kruminait e˙ and Zlatanova, 2014) (Zlatanova, Liu, and Sithole, 2013) are spaces where people are served by a spatial element or are waiting for services provided by the spatial unit. Therefore functional areas of objects appear in directions where services are provided. Moreover, certain objects might attract a larger number of people compared to others. For instance, queue of people may appear near the before mentioned information desk while coffee table is usually seated by a certain number of people. Table 2.2 lists some examples of criteria in indoor spaces subdivision. Specifically, the criteria includes six aspects: attractiveness, necessity, closeness to the central location, limited capacity, transition zone, and private space. The attractiveness, closeness to the central location, and transition zone have influence on the distribution of people within an area. For instance, the restaurants, cafeterias or shops in airports which

2.3. Criteria in Space Subdivision

23

are far away from gates are less attractive to travellers, since they want to spend the least efforts to reach these places before continuing their trips. But currently, the research are insufficient for pedestrian navigation from two aspects: (a) only objects in indoor spaces are considered. (b) These criteria only based on the places (e.g., train station, airport, etc.) rather than based on the pedestrian contexts, because the contexts decide which object should be take into account, and the number of a certain type pedestrian can have a big influence on the criteria.

25

Chapter 3

Proposed PhD Research 3.1

Problems in pedestrian navigation

It seems that navigation problems have been solved successfully in the past for most indoor spaces, outdoor spaces or both. Nevertheless, it is worth mentioning that the seamless indoor and outdoor navigation is still very limited. Related research contains navigation network models, indoor and outdoor seamless navigation, wayfinding algorithms and context-based navigation. Even the pedestrian route choices and way-finding behaviours have good outcomes. However, there are still several problems related with pedestrian navigation in indoor and outdoor space: 1. Current pedestrian navigations for indoor and outdoor are separated. We are living in a continuous space and world, and pedestrians can have activities in both indoor and outdoor space (Yang and Worboys, 2011a). Although the indoor environment is different from the outdoor environment, the essence of the space in indoor and outdoor is the same from the pedestrian navigation aspect, since the process for pedestrians to find the ways in both spaces is the same, i.e., they try to avoid physical obstacles to reach destination, and they only can walk in free space or navigable space. It is impossible for pedestrian to pass through physical objects, such like walls and furnitures in indoor, and trees in outdoor. However, pedestrians are not navigated seamlessly in indoor and outdoor, since current navigation works in two worlds, indoor world (space) and outdoor world (space). Seamless pedestrian navigation is still very limited, although some applications (e.g., Google Maps) have included some indoor spaces in pedestrian navigation, or say that they try to navigate pedestrians seamlessly. 2. The navigation network mainly designed for car navigation purposes rather than for pedestrian navigation. The ultimate reason of the limited seamless pedestrian navigation is the difference between different navigation networks. In outdoor space, the navigation network mainly designed for car navigation purposes rather than for pedestrian, although some applications try to reuse and adapt it for pedestrian navigation. But it still can bring in some the shortage that navigable space in outdoor for pedestrian navigation has been shrunken or expanded. For instance, pedestrians are navigated to make a detour in large space (e.g., the square), since there are no clear paths, but the reality is that pedestrians can across the square directly. Another example is that current application navigate pedestrians in the car roads, which is not allowed and dangerous for pedestrians. 3. Space subdivision related contexts are neglected.

26

Chapter 3. Proposed PhD Research In current navigation applications, pedestrians can only find some general objects (destinations), no matter what purposes (utilitarian walking, rambling, strolling, promenade, or for special events) they have. It means that the pedestrian related contexts are missed in current navigation applications, and the most direct reason is that these contexts are not included in the navigation network. If the navigation networks want to include these contexts, they, first of all, should be included in the division of space, because the navigation nodes come from sub-spaces. Different kinds of pedestrians are interested in different objects during their paths, and certain objects might attract a larger number of people compared to others. Furthermore, the sub-spaces for objects should include functional areas, because the functional spaces are not be occupied physically, but they are not passable.

Considering the above three questions comprehensively, we think the requirements for 3D space subdivision who can enable context-aware seamless indoor and outdoor pedestrian navigation should hold the following properties: spaces-oriented, sharing the same space-subdivision rules, and context-aware included. In other words, it should be a framework to construct 3D spatial navigation network model for pedestrian in indoor and outdoor, in which some contexts will be taken into account. Specifically, indoor and outdoor spaces will be deal with the same vision (the same structure, same management methods, as well as the same space subdivision rules, etc.), thus using the spaces in the same way, as there is no essential difference between the spaces from the perspective of pedestrians. In addition to space itself, it needs to be very clear about the names of the spaces different pedestrians interested in, and what space is when pedestrians are in. Moreover, the criteria are critical aspects. We can determine the objects included in space subdivision from contexts based on the types of pedestrians, but not only the object itself, but also the functional spaces should be formalised and included in the space subdivision, since the combination (objects + their functional spaces) act as the non-navigable spaces in pedestrian navigation. While determining the functional spaces, the criteria are indispensable.

3.2

Research questions

The general research outcome of this research is a framework to construct 3D spatial navigation model for pedestrian in indoor and outdoor, in which some contexts will be taken into account. It also should be noticed that there are two important hypothesis in this research: (a) the essence of the space in indoor and outdoor is the same for pedestrian navigation, although indoor environment is different from outdoor environment, and (b) compared to the cars, pedestrians have the maximum freedoms, which means that they can walk freely to somewhere vehicles cannot, especially the large space where has no clear patterns (or paths) for movements. Therefore, the main research question for this proposal is as follows: What 3D space subdivision can enable context-aware seamless indoor and outdoor pedestrian navigation? To answer this main research question, the research question is divided into three sub-questions: 1. Spaces. • What kind of spaces are interesting for pedestrians?

3.3. Scope of the research

27

• How to define and manage these indoor and outdoor space for pedestrian navigation? 2. Spaces-subdivision. • Is it possible to share the same subdivision rules in indoor and outdoor space? • How to enclose outdoor spaces for the subdivision? 3. Criteria. • What kind of criteria have influence on the space subdivision for pedestrian navigation in different situations? • How to effectively use the criteria to provide appropriate subdivision?

3.3

Scope of the research

• Navigation object is pedestrian, and the definition the pedestrian can be seen in the Chapter 3. • The focus will be on indoor and outdoor space 3D subdivision, rather than 2D, and navigation network construction approaches for seamless navigation will be investigated. • Contexts will be involved, but only only limited to determine which objects will be included in the space space subdivision. For instance, tourists are interested in the best camera points, benches, and open space can be used for picnic are interesting spaces for them, but these spaces are not interesting for pedestrians who want to walk with the dog for leisure only. Thus, the purposes and types of the pedestrians are contexts in this research, and based on the contexts, we can determine that the best camera points, benches, and open space can be used for a picnic will be included in the space subdivision for tourists, but not in the space subdivision for the pedestrians who walk with dogs for leisure. • The navigation paths will be computed in some cases at the end of this research to test the the space subdivision and navigation network construction. • This research only takes the CityGML1 , IndoorGML2 , point cloud, and IFC/BIM3 as the data sources, and tailor them to the needs of space management. • This research will not touch the positioning related topics, for example, GPS, sensors, positioning theories, methods, accuracy, and algorithms, etc. • Seamless indoor and outdoor navigation based on the system integration (e.g., integration of GPS and INS) is not included in this research. • The methods of constructing 3D indoor and outdoor geometry space models are not included in this research, but I will construct some 3D geometry space models manually for my experiments. 1

https://www.citygml.org http://indoorgml.net 3 https://www.solibri.com/support/bim-ifc/ 2

28

Chapter 3. Proposed PhD Research

Void Space

(a)

(b)

F IGURE 3.1: Walls in volumetric-based space model.

3.4 3.4.1

Methodology Spaces

Q1: What kind of spaces is interesting for pedestrians? Method: Identify the spaces taking pedestrian behaviours into account. For this question, we will take pedestrian behaviours into account to improve the space identification method (Pouke et al., 2016), because this question is related to the pedestrians, and the spaces why they are interesting for pedestrians, for instance, where pedestrians like to visit in certain spaces. Specifically, we will define what are the pedestrians, and investigate the reasons of their movement on foot, then summarise the spaces they are willing to go. Some rules will be concluded by analysing walking and resting habits of pedestrians to identify functional areas, such as where has a good eyesight, where is suitable for taking pictures, where is the best place for entertainment (e.g. barbecue), and where has benches, etc. Q2: How to define and manage these indoor and outdoor space for pedestrian navigation? Methods: Classify and define spaces, and then design a volumetric-based space model, schema, and data structures (tables in DBMS). First of all, we will define and classify the space, then a new unified volumetricbased space model (every objects will be regarded as spaces) will be put forward to represent the spaces, see Figure 3.1, the walls are separate objects or cells with certain thickness (Lee et al., 2014), and pedestrians cannot have activities in them. CityGML consider walls as thin (or ‘papers’), or consider the walls are part of the space, see Figure 3.2. IFC basically include the name, thickness, and material information of wall components (Fazio et al., 2007), but the IFC only for the buildings, which is not sufficient for both the indoor and outdoor pedestrian navigation. In concrete terms, all of the physical space and objects in indoor, outdoor, semi-indoor, and semi-outdoor space are represented as volumetric spaces, then we can get void space and occupied space by space computations. All the occupied space and part of the void space constitute the non-navigable space, while navigable space can be extracted from the void space according to the characteristics and needs of pedestrians. For the void space, part non-navigable space can be computed by the habits of pedestrians, e.g., pedestrians like to keep a certain minimum distance from walls and obstacles (Bosina et al., 2016), see Figure 3.3, which is a straight corridor where areas around walls, a vending point and a pillar are not used by pedestrians, illustrating the principle of separation distance.

3.4. Methodology

29

F IGURE 3.2: Wallsurface/‘paper’ wall in the CityGML (Gröger et al., 2008).

F IGURE 3.3: Example of the distance between people and wall in a straight corridor (Bosina et al., 2016).

3.4.2

Spaces-subdivision

Q1: Is it possible to share the same subdivision rules in indoor and outdoor space? Method: Use some rules (algorithms) based on variable size cells to subdivide both indoor and outdoor spaces. First of all, we consider such subdivision is feasible, because the indoor and outdoor is the same for pedestrian navigation, although the indoor environment is different from the outdoor environment. For instance, Figure 3.4 shows the possible subdivision approach for indoor space while Figure 3.5 is an example of 3D subdivision considering the terrain elevation. But we can image the furniture in indoor as the buildings in the outdoor spaces. In turn, we can also think outdoor buildings

F IGURE 3.4: Possible subdivision approach for indoor space (Diakité and Zlatanova, 2017).

30


(a)

(b)

(c)

F IGURE 3.5: Outdoor navigation network based on space subdivision (Rusné, Ljiljana, and Beirao, 2017).

(a)

(c)

(b)

(d)

F IGURE 3.6: Dimension of the pedestrian in 3D navigation network.

as indoor furniture. Thus, the spaces can be managed in the same way, i.e., all of the objects and void spaces as volumes (spaces), who have length, width, as well as height. After taking the pedestrians into consideration, the navigable spaces can be extracted. Space-subdivision rules (algorithms) based on variable size cells (Nagel et al., 2010) for both indoor and outdoor will be investigate, since it is more promising in computational efficiency for outdoor compared to the octree, tetrahedron, 3D voronoi diagrams, and voxels. Moreover, long or irregular indoor spaces (such as corridors, concave shapes) will be further partial subdivided, because one node does not represent well the structure or the way of movement in the space (Brown et al., 2013). On the other hand, in this research, pedestrians are 3D objects, who has a 3D buffer space. In the vertical direction, pedestrians have height, people will make a detour where lower than their height in normal condition. Q2: How to enclose outdoor spaces for the subdivision? Method: Employ some rules (parameters) based on navigation objects, and space features to enclose outdoor spaces for the subdivision. Outdoor spaces are not as sharp as the indoor spaces, so in order to subdivide

3.4. Methodology

31

outdoor spaces, the first step is to enclose them. We intend to enclose them based on two principles. The first principle is the actual size of the navigation objects, and the functional area. Pedestrians are 3D objects rather than dots, and their attributes include height, width, buffer distance, etc. Furthermore, their size is decided by the objects they carrying or using, e.g., the actual width of the pedestrian becomes the width of the cart for cleaning work. Figure 3.6, in which (a) is the Navigation scenario, (b) is a 2D navigation network, (c) is 3D navigation network, and (d) is an extended 3D navigation network based on the actual size of the navigation objects. The navigation object needs to move from the start (indoor) to the end (another indoor), in which also outdoor space is needed. Networks will be constructed as channels with three-dimensional attributes, rather than lines, Figure 3.6b. To some extent, they are like passable pipes, see Figure 3.6c, or extended pipes, see the pink parts in Figure 3.6d. The size of a pedestrian(s) is: P = {Lp , Wp , Hp }

(3.1)

T = {Lt , Wt , Ht }

(3.2)

E = {L, W, H}

(3.3)

where the Lp , Wp , Hp are the length, width, and height separately. And the size of objects (tools) for walk can be:

where the Lt , Wt , Ht are the length, width, and height separately. Then the actual size of the navigation object is:

in which L = max{Lp , Lt }, W = max{Wp , Wt }, H = max{Hp , Ht }. Another principle is based on the space definition and classification. For instance, the definition of semi-indoor space in this research is the space has top but not physically enclosed completely by man-made or natural materials/structures/objects (e.g., wall, stone, etc.), therefore, we can enclose this space by its top and the objects who support the top.

3.4.3

Criteria

Q1: What kind of criteria have influence on the space subdivision for pedestrian navigation in different situations? Method: Investigate criteria (e.g., attractiveness) based on contexts for the space subdivision. On one hand, we will improve the value range of the criteria (attractiveness, necessity, closeness to the central locations, limited capacity, transition zone, and ¯ ˙ 2014), see Table private space) for the space subdivision mentioned in (Kruminait e, 3.1. Because the value range is not very accurate, e.g., the attractiveness, there are only three levels, non-attractive, moderately attractive, and highly attractive, so it is very ambiguous the attractiveness is about 80% or 35%. We cannot tell this case should belong to which level. Another example is the limited capacity, which value ranges are only yes or no. Actually, any object has a limited capacity, or a unlimited capacity. For instance, a long bench without clear number of seats, which can hold many people can even other people can sit on the ground around the bench, or it can be occupied only one people who is sleeping. Therefore, the criteria decided by many factors, rather than only the physical features.

32

Chapter 3. Proposed PhD Research ¯ ˙ 2014). TABLE 3.1: Criteria and value ranges (Kruminait e,

Criterion

Measurement

Value range

Attractiveness

How inviting is the structure of the object?

1 - non-attractive; 2 - moderately attractive; 3 - highly attractive

Necessity

Is it necessary to have this object in this environment? Is it an important/essential feature of the environment?

0 - non-essential object; 1 - essential object

Object’s closeness to central locations

How close object is to all other surrounding objects?

[0-1] 0 - object is far away from other locations; 1 - object is close to other locations

Limited capacity

Does the object have limited number of seats?

Yes - object has limited capacity; No - object does not have limited capacity

Transition zone

Does the object provide services in a distance?

Numerical variable based on structure of the environment

Private space

What is the minimum distance that people keep in order not to violate others personal space in this environment?

Numerical variable based on type of the building (Hall’s personal, social distances)

On the other hand, more criteria will be investigated besides the above six, especially taking the pedestrian contexts into consideration, for example, the number of the pedestrians - whether only one pedestrian is needed to be navigated. Q2: How to effectively use the criteria to provide appropriate subdivision? Method: Formalise the criteria (e.g., attractiveness during the time by peak hour, and off-peak hour) for space subdivision. We will adapt some mathematical models to estimate the criteria in quantity ¯ ˙ 2014) is adopted from (Sevtsuk ways. For example, the closeness in (Kruminait e, and Mekonnen, 2012) where using network analysis method closeness of an input features is defined as the inverse of cumulative distance required to reach from specific node to all other nodes that fall within the search radius along the shortest paths. In other words it can be said that closeness of an object describes how far the object is from its surrounding neighbours. Therefore, closeness is estimated as: Closenessr [i] =

1 ⌃j2G

{i},d[i,j]r (d[i, j])

(3.4)

where the Closenessr [i] is the closeness of node within the search radius r, d[i, j] is the shortest path distance between nodes i and j. Anther example is the attractiveness of the information desk, see Figure 3.7. The functional (coloured) areas around the information desk are not non-attractive, moderately attractive, and highly attractive only, but can be calculated by some mathematical models. We plan to model it into four areas (every 0.25 meter, blue, green, yellow, and red) within 1 meter, since 1 meter is taken as a rule in a lot of places,

3.4. Methodology

33

F IGURE 3.7: Information desk with functional areas (coloured areas).

Point cloud

IFC/BIM

CityGML/IndoorGML

Volumetric-based space model

Functional Spaces

Database Management System

Criteria Object Space

Free Space

Navigation Network

Unoccupied Space

Navigation Nodes

∩ Occupied Space

Navigation object (Pedestrian)

Poincare Duality

Contexts

Impassable Space

Passable Space

Sub-spaces

Non-navigable Space

Navigable Space

Algorithms

F IGURE 3.8: Workflow of my research.

where they set a mark to remind people to wait outside this line. But it can be less if there are a lot people want the information desk with some special events.

3.4.4

Implementations

Figure 3.8 shows the workflow of this research. We plan to use 3D models (Point Cloud, IFC/BIM, CityGML/IndoorGML, etc.) and 2D model (Floor plans), because the Point Cloud can be a common data source, and 2D model is easy to obtained. More importantly, all of the models in this research is related to the semantics, which included in the models of IFC/BIM, CityGML, and IndoorGML. We will make full use of semantics in these models, rather than assign semantics to objects manually, because it will be very complex and time consuming. To be specific, all of the data (3D geometry space models, as well as navigation networks) will be managed in the database management system by designing spatial tables, in which the input data (Point Cloud, IFC/BIM, CityGML/IndoorGML, etc.) will be tailored to meet the needs of space management, for instance, using the method presented in (Diakité and Zlatanova, 2016b) to generate valid indoor spaces in IFC models. We can get the object space directly, then the free space can be extracted semantically rich and furnished IFC models (Diakité and Zlatanova,

34


(a) An imagery 3D bookstore

(b) Campus map of TU Delft

F IGURE 3.9: Planned experimental scenarios in this research.

2016a). The functional space can be determined from object space based on some criteria, e.g., attractiveness, which is responsible for the formation of groups of people since density of individuals increases around particularly attractive places. The union of functional space and object space is occupied space, which is also part of the non-navigable space. While the unoccupied space (free space) can be separated into impassable space and passable space based on the navigation object (pedestrians, or pedestrian with some tools). Navigable space will be subdivided into sub-spaces, and the occupied space can be some sub-spaces based on the contexts (the kind of pedestrians). Then the navigation nodes and links can be derived from sub-spaces based on the Poincaré Duality theory. The final navigation network also stored and managed by the database management system. Furthermore, two indoor and outdoor scenarios will be taken for case studies , see Figure 3.9, an imagery 3D bookstore and the campus of the TU Delft. Specifically, we will choose the building of Faculty of Architecture and the Built Environment and the building of Civil Engineering and Geosciences as the indoor spaces, and the outside spaces between them are outdoor spaces.

3.5 3.5.1

Preliminary Results Pedestrian definition

There are many definitions about the pedestrian, in the Wikipedia4 , a pedestrian is a person traveling on foot, whether walking or running. In some communities, those traveling using tiny wheels such as roller skates, skateboards, and scooters, as well as wheelchair users are also included as pedestrians. In modern times, the term usually refers to someone walking on a road or pavement. In the Oxford Dictionaries5 , the pedestrian is a person who is walking rather than travelling in a vehicle. In the Merriam-Webster6 , pedestrian is going or performed on foot. From the pedestrian navigation perspective, we define the pedestrian as a person traveling on foot, whether walking or running. Or a robot who can mimic people, also traveling on foot. As for those persons or robots, they are not pedestrians when travelling using tiny wheels such as roller skates, skateboards, and scooters, or wheels such as self-balancing scooter, because they have lost the flexibility of 4

https://en.wikipedia.org/wiki/Pedestrian https://en.oxforddictionaries.com/definition/pedestrian 6 https://www.merriam-webster.com/dictionary/pedestrian 5

3.5. Preliminary Results

35

Pedestrian Walking

Running

Robot walking or running on foot

Pedestrian + Tiny wheel tools

People with scooter

Roller skating people

Skateboarding people

Pedestrian + Wheel tools Walking with trolley

Self-balancing scooter

Self-balancing vehicle

F IGURE 3.10: Proposed definition and types of the pedestrians. Semi-Indoor space

No Yes

Spaces

Indoor space or Semi-indoor space

Enclosed completely

Outdoor space or Semi-outdoor space

Go through the entrance

Yes

Indoor space

Top closed

No

No

Outdoor space

Yes Semi-outdoor space

F IGURE 3.11: The flow chart of defining the spaces.

pedestrian movement, for instance, pedestrians can walk on stairs, but those persons or robots have to quit from these tiny wheel tools or wheel tools and back to pedestrian (traveling on foot). Therefore, the pedestrian without any tools have the maximum flexibility from the point of view of the pedestrian navigation, but with the tiny wheel tools or wheel tools, they prefer to move on flat space, while for the tools with wheel, the flatter the better, see Figure 3.10.

3.5.2

Spaces classifications and definitions

The definitions of the space share four unanimously approved characteristics: boundless, extensible, three-dimensional, and can be occupied, see Table 2.1. From the pedestrian navigation view, these four features are very precise. Therefore, the definition of the space is that it a boundless, extensible, and three-dimensional area in which other objects can occupy. In this research, we classify the space into four types based on the structure, i.e., Space = Indoor space

S

Outdoor space

S

Semi indoor space

S

Semi outdoor space

The most intuitive definition of indoor space is a space bounded by the wall, floor and roof. More importantly, the core of indoor space lies in the interior empty parts, rather than those shells (walls, floor, roof, etc.). People can have activities, behaviours, etc. in these interior empty parts (indoor space). We put forward a definition system based on three criteria from the perspective of structures. Figure

36


(a)

(b)

F IGURE 3.12: Roof and top in the building.

3.11 shows how to define and distinguish the spaces based on the structure and the way to visit the space. The first judgement is the top, if the space has a top closed structure, it can be Indoor space or Semi-indoor space. Then whether the space is enclosed completely is the second judgement, the space physically enclosed completely by man-made or natural materials/structures/objects (e.g., wall, stone, etc.) is Indoor space, otherwise it is Semi-indoor space. While for the space without top closed, can be Outdoor space or Semi-outdoor space. The last criterion is the way of access, if it is necessary to pass some entrances to visit the space, it will be the Semi-outdoor space, otherwise, Outdoor space. There are some definitions in the process of defining the space. Roof: A roof is part of a building envelope. It is the covering on the uppermost part of a building or shelter which provides protection from animals and weather, notably rain or snow, but also heat, wind and sunlight. The word also denotes the framing or structure which supports that covering. Top: A top is something above the ground, and people can do something under it, but it is not always can provide protection from animals and weather, for example, some top can help people escape from the sunlight, but cannot for the rain. Roof is one kind of the Top, but Top maybe not Roof, see the parts circled by red rectangle in Figure 3.12, in which the (a) is Roof, while (b) is Top. Enclosed completely: physically enclosed completely by man-made or natural materials/structures/objects (e.g., wall, stone, etc.), rather than Side and Top. In summary, our definitions of spaces can be following (Figure 3.13): Space is a boundless, extensible, and three-dimensional area in which other objects can occupy. Indoor space is the space physically enclosed completely by man-made or natural materials/structures/objects (e.g., wall, stone, etc.). Outdoor space is the space without the top closed, nor the side closed, and people can visit it without going through any entrance. Semi-indoor space is a space with the top closed, but not enclosed completely like the indoor space. Semi-outdoor space is a space without the top closed, and people have to go through the entrance to visit it. We can find obvious features of different spaces based on our definition, but this is actually a non-discrete problem, especially for different situation in the pedestrian navigation. Figure 3.14 lists the features and the relationships between four kinds of spaces, in which the gradient blue bar indicates the possibility of the structure being presented, the deeper the colour, the greater the possibility. The black means


37

Environment

Indoor

Semi-indoor

Semi-outdoor

Outdoor

Definition

With top & Enclosed completely

With top & not enclosed completely

Without top & be visited by going through entrance

Without top & can be visited without going through any entrance

Example

Scene

F IGURE 3.13: Our defnitions of the spaces.

Roof

Top

Indoor

Door

Top

Semi-indoor

Entrance

Semi-outdoor

Entrance

Outdoor

Entrance

F IGURE 3.14: Features of the spaces.

pretty sure, while the blue means maybe, or even no. The indoor space physically enclosed completely by man-made or natural materials/structures/objects, and it is very sure that it has roof and door. But for the semi-indoor, we can find that the function of the top are getting closer to the roof. The reason why we need to distinguish this is that there are some tops only used for decorations, which cannot be used to protect pedestrians from rain, or snow, even sunlight, and the space with this kind of top almost is semi-outdoor. While for the entrance, the semi-outdoor must have an entrance, and indoor space have it (door) surely, but for semi-indoor space, it could have an entrance. What’s more, even the structure has the same function, it can be more than one type, see the bridge between two buildings (Figure 3.15). (a) is indoor space, but (b) is semi-indoor space, while (c) is semi-outdoor space, because the bridge spaces in (a) and (b) have tops, but the former is enclosed completely, while the latter is not, therefore, the former is indoor, and the latter is semi-indoor.The bridge space in (c) has no top, and people have to through the entrance (or door) to get there, so it is semi-outdoor space. Some examples in real environment in Figure 3.16. The number and name are as follows: (a) Room; (b) Open air; (c) Courtyard; (d) Roof terrace with sides; (e) Roof terrace with top; (f) Roof terrace with outside stairs; (g) Gas station; (h) Under the house eaves; (i) Under the house eaves; (j) Under an overpass; (k) Yard with fence; (l) Road with side fence; (m) Porch; (n) Bus stand; (o) Stadium. Base on our definitions, the 3.16(a) is obviously an indoor space, while the space in 3.16(b) is outdoor space, because the room is enclosed completely by walls, floor,

38


(a) Indoor

(b) Semi-indoor

(c) Semi-outdoor

F IGURE 3.15: Bridges between two buildings.

and celling, while the second scene is open air. 3.16(c), 3.16(k), and 3.16(l) are roof terraces, but the first one is semi-indoor space, because it has top, while the second and the third is semi-outdoors, since they have no top, but pedestrians have to traverse some entrances to visit them. Because of the roof, the gas station 3.16(d), under the house eaves 3.16(e) and 3.16(f), under the overpass 3.16(g), porch 3.16(h),and bus stand 3.16(i) are semi-indoor spaces. However, the courtyard 3.16(j), although surrounded by adjacent buildings, it is still a semi-outdoor space, because it has no top closed, and pedestrians have to through the entrance to get in it. Moreover, the yard with fence 3.16(m), and the road with side fence 3.16(n) are semi-outdoor spaces. For the football court within the stadium 3.16(o) is semi-outdoor space in our definition, since it has no top, and pedestrians have to through some entrances to visit it. Therefore, the conclusion of this part is that our definitions are feasible and effective, because any space can find right space type in our definitions.

3.5.3

Spaces Management

In this research, all of things are spaces (Volumes), see Figure 3.17. The general notion of the space is Spaces, which includes Indoor Space, Outdoor Space, Semiindoor Space, and Semi-outdoor Space. Void Space can exist in all the four types of spaces. In Indoor Space, Floor, Furniture, and Wall are Non-Navigable Space, while Door, Window, Connection, and Void Space are Navigable Space. The Floor is a separate space, which cannot be through, but Furniture can stand on. The Connection is a general concept of objects who can connect different spaces, such as Stair, Escalator, Elevator, and Lift. In Outdoor Space, Ground, CityFurniture, Waterbody, and some of Vegetation (e.g., trees) are Non-Navigable Space, but some of vegetation (e.g., lawn) are Navigable-space, in which the Ground is also a separate space, and other space can be on or in it, e.g., Building can inside the Ground or on the Ground. Bridge, Tunnel, as well as Building are Navigable Space, although sometimes, they can be closed because of maintains, or emergency situations. Moreover, Navigable Space are Changeable Space, since it can be changed into Non-Navigable Space, for example the Door is closed. Window also is Non-navigable space in non-emergency situations. Semi-indoor/outdoor Space is a bit of special, and only Void Space is considered.

3.5.4

Spaces Computation

We did void space computation experiments on the imagery 3D bookstore, and all of them are computed automatically by Rhino + Grasshopper.


39

(a) Indoor

(b) Outdoor

(c) Semi-indoor

(d) Semi-indoor

(e) Semi-indoor

(f) Semi-indoor

(g) Semi-indoor

(h) Semi-indoor

(i) Semi-indoor

(j) Semi-outdoor

(k) Semi-outdoor

(l) Semi-outdoor

(m) Semi-outdoor

(n) Semi-outdoor

(o) Semi-outdoor

F IGURE 3.16: Some examples of the spaces.

40

Chapter 3. Proposed PhD Research Spaces + absoluteHeight:float

Indoor Space

Ceiling (volumetric) Floor (volumetric)

Semi-indoor Space

Wall (volumetric)

Furniture (volumetric)

Semi-outdoor Space

Bridge (volumetric)

Connection (volumetric) Window (volumetric)

Stair (volumetric)

Outdoor Space

Tunnel (volumetric)

Building (volumetric)

CityFurniture (volumetric)

Vegetation (volumetric)

WaterBody (volumetric)

Grass (volumetric)

Escalator (volumetric)

Tree (volumetric)

Elevator (volumetric) Lift (volumetric)

Door (volumetric)

Convertible Space (volumetric)

Void Space (volumetric)

Non-navigable Space (volumetric)

Navigable Space (volumetric)

Volumetric + Horizontal + Vertical + Gradient 1..* Coordinates +x: float +y: float +z: float

Vertex 1

0..1

+ elevation: float

Edge 2

1

+ start: Vertex + end: Vertex

ConvexSpace

Triangle 3

0..2

+edges: Edge

1..*

1

+ triangles: Triangle [1…] + edges: Edge [3…]

F IGURE 3.17: The volumetric-based data model of spaces.

(a)

(b)

(c)

(d)

(e)

(f)

F IGURE 3.18: Experiment results of the imagery 3D bookstore.

Ground (volumetric)


41

Some results can be seen in Figure 3.18, in which (a) is the original 3D indoor and outdoor space. (b) is the void space including large space, road, square and bridge. (c) is the void space of indoor, while (d) is 3D void space of indoor. (e) is the void space the of the whole in the scenario, including indoor and outdoor. (f ) is the whole space with colours in the scenario. However, current experiments are some preliminary attempts and they have many disadvantages. For instance, the semantics of the objects have to be assigned manually, because the original data is 2D floor plans, in which only geometry can be obtained. Another shortcoming is that the current data is very small, so it can be constructed manually, but for large scene, all objects in campus of the TU Delft, it will be very time consuming to get the 3D volumetric-based model. I plan to construct only two building with indoor structure, (the building of Faculty of Architecture and the Built Environment, and the building of Civil Engineering and Geosciences) and demonstrate the navigation path.

43

Chapter 4

Practical Issues 4.1

Courses & Supervision TABLE 4.1: The courses in the first year.

Discipline-related skills Courses

Start Date

End Date

Credits

Status

14/11/2016

30/01/2017

5 EC

Completed

16/11/2016

01/02/2017

5 EC

Completed

Courses

Start Date

End Date

Credits

Status

Problem-Solving & Decision-Making in Research Presenting Scientific Research Developing Your Academic Skills: Critical/Analytical Thinking and Scientific Reflection

07/12/2016

14/12/2016

1.5

Completed

23/01/2017 03/05/2017

02/02/2017 17/05/2017

3.0 1.5

Completed Completed

Start Date

End Date

Credits

Status

12/01/2017 24/01/2017 18/11/2016 09/01/2017

12/01/2017 24/01/2017 18/11/2016 16/01/2017

0.5 1.0 0.5 2.0

Completed Completed Completed Completed

14/03/2017 19/05/2017 06/06/2017

18/04/2017 15/06/2017 16/06/2017

3.0 1.5 2.0

Completed Completed Completed

GEO1006 Geo Database Management Systems GEO1003 Positioning and Location Awareness

Research skills

Discipline-related skills Courses PhD Start-up Module A PhD Start-up Module B PhD Start-up Module C Self-Awareness and autonomy in the research process The Art of Presenting Science Time Management How to Interact Effectively with Your Research Team

To complete the Doctoral Education programme, Jinjin Yan must earn 45 GS credits (1 GS credit = 8 hours of coursework + 4 hours of preparation/assignment). The programme is divided into three skills categories. The total 45 GS credits are divided equally among these categories: Discipline-related skills (15 GS credits) The discipline-related skills category focuses on giving him a greater breadth and depth of knowledge in the field of your doctoral research. Research skills (15 GS credits) The focus of the research skills category is to improve his ability to conduct scientific research, and improve the skills needed for a role as a researcher in an academic

44

Chapter 4. Practical Issues

environment. Transferable skills (15 GS credits) The transferable skills category focuses on personal and professional development, which will help him now and in his future career. Jinjin Yan will follow 12 courses (Table 4.1) at the first year, including 2 courses from MSc programmes, Geo Database Management Systems and Positioning and Location Awareness, and 10 courses from Graduate school. If all courses are qualified, Jinjin will get 26.5 GS credits. Jinjin will register Popular Scientific Writing (C5.M3), Writing a Dissertation (C13.M1), Writing a Scientific Article in English (C12.M4), and Creative Writing: Overcoming Writer’s Block (C12.M3) to improve the English writing skills and get 18.5 GS credits at least. Moreover, Prof. Jantien Stoter will act as the promotor and spend approximately 35 hours of supervision per year. On average, we will meet and have a communication about the research at least once per month. Dr. Sisi Zlatanova acts as the (co)promotor and daily supervisor. She will discuss with Jinjin Yan once per week about progress and problems of current research and the total time on the supervision is approximately 70 hours per year. In average, Sisi and the Jinjin will meet at least biweekly. Before each weekly or monthly meeting, Jinjin will prepare an agenda and submit a Weekly Progress Monitor report to the supervisors by email. Jinjin will take notes for both monthly and weekly meetings and summarise them as minutes and actions every time. During the sabbatical leave of the supervisors, the monthly or weekly meetings will take place by telephone or E-mail contact.

4.2

Software, Tools and Data

According to the objectives and methodology of this study, the following skills/tools are required: - Programming skills - Existing Standards etc. - Data Processing - Data Storage - GIS Skills

C++, C, Python, and Java etc. ISO, and OGC (GML, CityGML, IndoorGML), IFC/BIM

Matlab, SPSS, SQL etc.

Oracle Spatial, PostGIS etc. FME, ArcGIS etc.

- Model Generation and Visualization Grasshopper etc.

AutoCAD, 3Ds Max, Sketch Up, Rhinoceros,

- Datasets An imagery 3D bookstore; The campus of TU Delft, and the indoor spaces will include the building of Faculty of Architecture and the Built Environment and the building of Civil Engineering and Geosciences. - Others Visualization devices (e.g. mobile platforms), communication tools (wireless to mobile platforms) and indoor positioning equipment.

45

Chapter 5

Deliverables 5.1

Outcomes and Reports

It is expected that a number of articles and papers will be prepared for international journals and conferences during the whole phases: • 2 or 3 papers per year in (international) conferences. e.g. ISPRS, UDMS, Gi4DM, SDH, 3D GeoInfo etc. • 2 or 3 papers in reviewed scientific journals. Potential conferences and Journals e.g. IJGIS, Sensors, GeoInformatica, Computers, Environment and Urban Systems, etc. Except for the output in the form of articles and papers, other types of outputs are to be anticipated from this research: A framework to construct 3D spatial navigation model for pedestrian in indoor and outdoor, in which some contexts will be taken into account. Specifically, • A classification and definition method for spaces. • Volumetric-based space model, schema, and data structures (tables in DBMS). • The space identification methods taking pedestrian behaviours into account. • Space-subdivision rules (algorithms) based on variable size cells for both indoor and outdoor. • Rules (parameters) to enclose outdoor spaces for the subdivision. • Criteria (e.g., attractiveness) for the space subdivision. • Algorithms to formalise the criteria(e.g., the time by peak hour, and off-peak hour) for space subdivision.

5.2

Time Planning

This research will be conducted over a period of 4 years. The following schedule is just a rough one. It is a time arrangement on the PhD research, which officially started on October 15th, 2016. The actual time may be adjusted as the research progresses. Figure 5.1 provides an overview of the planning activities that have been carried out and are to be done in this research. The research themes presented in the time planning overview are not strictly sequential, and will be investigated with a high degree of overlap in time. The plan is subject to change according to the flow of the research. Several tasks and activities will be a part of this research:

46

Chapter 5. Deliverables

Initial literature review. Writing proposal, Go/No Go meeting, Spaces definition & Management, Spaces-subdivision for spaces, Context definition & Management, Landmark-based guidance, Case studies, and Formalisation & Writing dissertation. Time and Items

2016 - 2017 Q1

Q2

Q3

Q4

2017 - 2018 Q1

Q2

Q3

Q4

2018 - 2019 Q1

Q2

Q3

Q4

2019 - 2020 Q1

Q2

Q3

Q4

Initial literature review Early-stage preparations

Writing proposal Go / No Go meeting The space identification methods based on pedestrian behaviours

Space

A classification and definition method for spaces Volumetric-based space model, schema, and data structures

Space subdivision

Criteria

Space-subdivision rules based on variable size cells Rules (parameters) to enclose outdoor spaces for the subdivision Algorithms to formalise the contexts for space subdivision Criteria (e.g., attractiveness) for the space subdivision

Prove of concept & Implementations & Case studies dissemination Formalisation & Writing dissertation

F IGURE 5.1: Time planning overview.

5.3

Short term first year plan

This section presents the proposed key events and milestones achieved in the first year of research. The first year besides writing proposal includes writing two general research papers and attending four events. Table 5.1 enlists the key events of the first year of research. TABLE 5.1: Key events in the first year of research.

Month

Key Event

2016-10 2016-11 2017-02 2017-03 2017-04 2017-05 2017-06 2017-07 2017-09

Start of research GEO1003 & GEO1006 Preliminary results 1 Writing abstract for ISPRS Workshop 2017 (accepted) Preliminary experiments Writing full paper for ISPRS Workshop 2017 Writing draft of the journal paper Preliminary results 2 Poster for ISPRS Workshop 2017 & Go/No go meeting

5.4

Publications

In the first year, two publications, one for conference, and another for journal, will be submitted. Challenges in Flying Quadrotor Unmanned Aerial Vehicle for 3D Indoor Reconstruction, Jinjin Yan, Nives Grasso, Sisi Zlatanova, Robin Christian Braggaar and Danny Marx. ISPRS Workshop Indoor 3D 2017. (Abstract accepted). Spaces from the Perspective of Pedestrian Navigation, Jinjin Yan, Sisi Zlatanova, Abdoulaye Diakité. (In preparation).

47

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