Simulation-Based Evaluation of Ease of Wayfinding Using Digital Human and As-Is Environment Models International Journal of Geo-Information Article Simulation-Based Evaluation of Ease of Wayfinding Using Digital Human and As-Is Environment Models Tsubasa Maruyama 1,*, Satoshi Kanai 2, Hiroaki Date 2 and Mitsunori Tada 1 1 National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan; m.tada@aist.go.jp 2 Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan; kanai@ssi.ist.hokudai.ac.jp (S.K.); hdate@ssi.ist.hokudai.ac.jp (H.D.) * Correspondence: tbs-maruyama@aist.go.jp; Tel.: +81-3-3599-8201 Received: 30 June 2017; Accepted: 24 August 2017; Published: 26 August 2017 Abstract: As recommended by the international standards, ISO 21542, ease of wayfinding must be ensured by installing signage at all key decision points on walkways such as forks because signage greatly influences the way in which people unfamiliar with an environment navigate through it. Therefore, we aimed to develop a new system for evaluating the ease of wayfinding, which could detect spots that cause disorientation, i.e., “disorientation spots”, based on simulated three-dimensional (3D) interactions between wayfinding behaviors and signage location, visibility, legibility, noticeability, and continuity. First, an environment model reflecting detailed 3D geometry and textures of the environment, i.e., “as-is environment model”, is generated automatically using 3D laser-scanning and structure-from-motion (SfM). Then, a set of signage entities is created by the user. Thereafter, a 3D wayfinding simulation is performed in the as-is environment model using a digital human model (DHM), and disorientation spots are detected. The proposed system was tested in a virtual maze and a real two-story indoor environment. It was further validated through a comparison of the disorientation spots detected by the simulation with those of six young subjects. The comparison results revealed that the proposed system could detect disorientation spots, where the subjects lost their way, in the test environment. Keywords: wayfinding; digital human model; signage; laser-scanning; structure-from-motion; accessibility evaluation 1. Introduction It is increasingly important in our rapidly aging society [1] to perform accessibility evaluations for enhancing the ease and safety of access to indoor and outdoor environments for all people, including the elderly and the disabled. Under international standards [2], “accessibility” is defined as “provision of buildings or parts of buildings for people, regardless of disability, age or gender, to be able to gain access to them, into them, to use them and exit from them.” As recommended in the ISO/IEC Guide 71 [3], accessibility must be assessed considering both the physical and cognitive abilities of individuals. From the physical viewpoint, for example, tripping risks in an environment [4] must be assessed to ensure the environment is safe to walk in, as conducted in our previous study [5]. By contrast, from the cognitive aspect, ease of wayfinding [6] must be assessed to enable people to gain access to destinations in unfamiliar environments. Wayfinding is a basic cognitive response of people trying to find their way to destinations in an unfamiliar environment based on perceived information and their own background knowledge [7]. Visual signage influences the way in which people unfamiliar with an indoor environment navigate through it [8]. As shown in Table 1, visual signage can be classified into positional, directional, routing, and identification signage depending on the type of navigation information on the signage. As recommended in the guidelines [2], these four types of signage must be arranged appropriately ISPRS Int. J. Geo-Inf. 2017, 6, 267; doi:10.3390/ijgi6090267 www.mdpi.com/journal/ijgi http://www.mdpi.com/journal/ijgi http://www.mdpi.com http://dx.doi.org/10.3390/ijgi6090267 http://www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2017, 6, 267 2 of 22 at key decision points considering the relationship between the navigation information on signage and the path structure of the environment. In addition, as mentioned in the literature [9], ease of wayfinding must be evaluated considering not only signage continuity, visibility, and legibility but also signage noticeability. Table 1. Signage type and navigation information. Signage Type Navigation Information Positional signage Next goal position to be reached to arrive at a destination (e.g., map) Directional signage Next walking direction to take to reach a destination (e.g., right or left) Routing signage Walking route to be taken to reach a destination (e.g., route drawn on map or indicated by textual information) Identification signage Name of current place Currently, ease of wayfinding is evaluated using four approaches: real field testing [10], virtual field testing [11,12], CAD model analysis [13], and wayfinding simulation [14–20]. In real field tests [10], a certain number of human subjects are asked to perform experimental wayfinding tasks in a real environment. By contrast, in virtual field tests [11,12], subjects are asked to perform wayfinding tasks in a virtual environment using virtual reality devices. In these real or virtual field tests [10–12], ease of wayfinding is evaluated by analyzing subjects’ responses to a questionnaire and their wayfinding results, e.g., walking route, gaze duration, and gaze direction. However, in these tests, prolonged wayfinding experiments involving a variety of wayfinding tasks must be conducted by various human subjects of different ages, genders, body dimensions, and visual capabilities. Thus, field tests are not necessarily efficient and low-cost approaches. In CAD model analysis [13], signage continuity is evaluated by analyzing the relationships among various pieces of user-specified navigation information indicated by signage. However, this approach cannot evaluate ease of wayfinding in terms of signage visibility, legibility, and noticeability because three-dimensional (3D) interactions between individuals and signage are not considered. Recently, a variety of wayfinding simulations has been proposed [14–20]. Such simulation-based approaches have made it possible to evaluate the ease of wayfinding by simulating the wayfinding of the pedestrian model. However, these simulations consider only a part of signage factors such as signage location, continuity, visibility, legibility, and noticeability. In addition, these simulations involve only simplified as-planned environment models that do not model the detailed environmental geometry, including obstacles on the walkway, and realistic environmental textures. For reliable evaluation, an environment model must be created to reflect the as-is situation of the environment because detailed 3D geometry and realistic textures affect the wayfinding of individuals [17,21]. Given the above background, the purpose of this study is to develop a new system for evaluating ease of wayfinding. The system makes it possible to detect spots that cause disorientation, i.e., “disorientation spots”, based on simulated 3D interactions among realistic wayfinding behaviors, as-is environment model, and realistic signage system. In this study, the as-is environment model represents an environment model that reflects a given environment as-is, i.e., detailed 3D geometry including obstacles and realistic textures. A schematic of the proposed system is shown in Figure 1. To achieve this goal, we draw on the results of our previous studies, in which algorithms of as-is environment modeling [22], walking simulation of a digital human model (DHM) in that environment model [23], and basic wayfinding simulation of the DHM [24] were developed. As shown in Figure 1, first, the as-is environment model consisting of the walk surface points WS, navigation graph GN , and textured 3D environmental geometry GI is automatically generated from 3D laser-scanned point clouds [22] and a set of photographs of the environment [24]. Then, a set of signage entities is created by the user by manually assigning signage information. Then, a wayfinding simulation scenario is specified manually by the user. Thereafter, the DHM commences its wayfinding in accordance with the navigation information indicated by the arranged signage, while ISPRS Int. J. Geo-Inf. 2017, 6, 267 3 of 22 estimating signage visibility, noticeability, and legibility based on imitated visual perception. As a result, disorientation spots are detected. ISPRS Int. J. Geo-Inf. 2017, 6, 267 3 of 22 while estimating signage visibility, noticeability, and legibility based on imitated visual perception. As a result, disorientation spots are detected. Figure 1. Overview of system for evaluating ease of wayfinding. The proposed system is demonstrated in a virtual maze and a real two-story indoor environment. The system is further validated by comparing the disorientation spots detected by the simulation with those obtained in a test involving six young subjects in the two-story indoor environment. The rest of this paper is organized as follows. Section 2 introduces the related literature and clarifies the contributions of this study. Section 3 presents a brief introduction of the previously developed as-is environment modeling system [22,24]. In Section 4, an overview of signage entity creation is described. In Section 5, the algorithm for the simulation in which DHM performs wayfinding is introduced. Finally, in Section 6, the system is demonstrated and validated. 2. Related Work This study is related primarily to wayfinding simulation research. A variety of simulation algorithms aiming to evaluate the ease of wayfinding have been studied. Chen et al. [14] proposed a wayfinding simulation algorithm based on architectural information such as egress width, height, contrast intensity, and room illumination in a 3D as-planned environment model. Furthermore, Morrow et al. [15] proposed an environmental visibility evaluation system using 3D pedestrian model. In the study, environmental visibilities from Figure 1. Overview of system for evaluating ease of wayfinding. The proposed system is demonstrated in a virtual maze and a real two-story indoor environment. The system is further validated by comparing the disorientation spots detected by the simulation with those obtained in a test involving six young subjects in the two-story indoor environment. The rest of this paper is organized as follows. Section 2 introduces the related literature and clarifies the contributions of this study. Section 3 presents a brief introduction of the previously developed as-is environment modeling system [22,24]. In Section 4, an overview of signage entity creation is described. In Section 5, the algorithm for the simulation in which DHM performs wayfinding is introduced. Finally, in Section 6, the system is demonstrated and validated. 2. Related Work This study is related primarily to wayfinding simulation research. A variety of simulation algorithms aiming to evaluate the ease of wayfinding have been studied. Chen et al. [14] proposed a wayfinding simulation algorithm based on architectural information such as egress width, height, contrast intensity, and room illumination in a 3D as-planned environment model. Furthermore, Morrow et al. [15] proposed an environmental visibility evaluation system using 3D pedestrian model. In the study, environmental visibilities from pedestrian models were evaluated to assist facility managers in designing architectural layout and signage placement. However, these ISPRS Int. J. Geo-Inf. 2017, 6, 267 4 of 22 studies [14,15] are not applicable to the evaluation of ease of wayfinding based on signage system because the pedestrian models used in them were not modeled to incorporate the surrounding signage in the simulation. Hajibabai et al. [16] proposed a wayfinding simulation using directional signage in an as-planned 2D environment model for emergency evacuation during a fire. The 2D pedestrian model used in the study could make decisions about its walking route based on perceived signage and fire propagation. However, in that study, signage visibility and legibility were estimated by oversimplified human visual perception, and signage noticeability was not considered. In addition, performing a precise 3D wayfinding simulation using a 3D as-is environment model using their framework is infeasible. Recently, signage-based 3D wayfinding simulation has been advancing. Brunnhuber et al. [17] and Becker-Asano et al. [18] proposed schemes for wayfinding simulation using directional and identification signage in a 3D as-planned environment model. In these simulations, the next walking direction of the pedestrian models was determined autonomously based on the navigation information on the perceived signage. Signage perception was realized by estimating signage visibility and legibility based on the imitated visual perception of the pedestrian model. However, signage noticeability was not considered in these simulations, although it has a significant effect on the wayfinding of people in unfamiliar environments [9]. More recently, advanced approaches for estimating suitable signage locations have been proposed. Zhang et al. [19] proposed a system for planning the placement of directional signage for evaluation. In their system, a minimum number of signage and appropriate signage locations were determined automatically by simulating interactions between the pedestrian models and the signage system. In addition, Motamedi et al. [20] proposed a system for optimizing the arrangement of directional and identification signage in building information model (BIM)-enabled environments. Their system estimated optimal signage arrangement based on signage visibility and legibility for a 3D pedestrian model walking in a BIM-based environment model. However, as in cases of other previous simulations, signage noticeability was not considered in these studies [19,20]. In addition, the system [19] was validated with an oversimplified environment model imitating a large rectangular space having an egress, and the feasibility of its use in realistic and complex as-is environments was not validated. By contrast, in the system [20], the walking route of the pedestrian model was not changed based on the navigation information indicated by perceived signage, so evaluation based on signage continuity was basically infeasible. Furthermore, these simulations [16–20] treated only one or two types of signage—Directional and/or identification. Thus, these simulations cannot be applied to actual signage systems including all signage types in Table 1. Moreover, with the exception of the simulation proposed by Motamedi et al. [20], simplified as-planned environment models were used in the previous wayfinding simulations. Therefore, to realize a reliable evaluation of ease of wayfinding, simulation users and/or facility managers are urged to create detailed and realistic as-planned environment models, including small obstacles and environmental textures based on measurements of the environment. Unlike the simulations developed in these previous studies [14–20], the proposed system can evaluate the ease of wayfinding by simulating 3D interactions among realistic wayfinding behaviors, as-is environment model, and realistic signage system. Specifically, the contributions of the present study are as follows: 1. DHM can make a decision based on the surrounding signage perceived by its imitated visual perception in consideration of signage location, continuity, visibility, noticeability, and legibility. 2. As-is environment model including detailed environmental geometry and realistic textures, can be generated automatically using 3D laser-scanning and SfM. 3. Proposed system can simulate the wayfinding of the DHM by discriminating among four types of signage, namely, positional, directional, routing, and identification signage. ISPRS Int. J. Geo-Inf. 2017, 6, 267 5 of 22 4. Proposed system is validated through a comparison of disorientation spots between simulations and measurements obtained from young subjects. 3. Automatic 3D As-Is Environment Modeling In the proposed system, first, an as-is environment model is generated automatically. As shown in Figure 2, the model comprises walk surface points WS, a navigation graph GN , and textured 3D environmental geometry GI . WS represents a set of laser-scanned point clouds on walkable surfaces such as floors, slopes, and stair-treads. Specifically, WS is used to estimate the footprints of the DHM during the simulation. GN generated from WS represents the environmental pathways that the DHM would navigate through during the simulation. The graph GN = 〈V, E, c, t, ES〉 comprises a set of graph nodes V and a set of edges E. Each node vk ∈ V represents free space in the environment, and has a position vector t(vk) and cylinder attribute c(vk), whose radius r(vk) and height hv represent the distance to the wall and walkable step height, respectively. Each edge ek, representing the connectivity of free spaces, is generated between two adjacent nodes with a common region. ES = {esk} represents a set of stair edges connecting two graph nodes at the end of stairs. WS and GN can be generated automatically using our method [22]. By contrast, GI represents a 3D mesh model with high-quality textures, and it is used to estimate signage visibility and noticeability during simulation. GI can be created automatically using SfM with a set of photographs of the environment [24]. Detailed algorithms and demonstrations are given in our previous studies [22–24]. ISPRS Int. J. Geo-Inf. 2017, 6, 267 5 of 22 4. Proposed system is validated through a comparison of disorientation spots between simulations and measurements obtained from young subjects. 3. Automatic 3D As-Is Environment Modeling In the proposed system, first, an as-is environment model is generated automatically. As shown in Figure 2, the model comprises walk surface points , a navigation graph , and textured 3D environmental geometry . represents a set of laser-scanned point clouds on walkable surfaces such as floors, slopes, and stair-treads. Specifically, is used to estimate the footprints of the DHM during the simulation. generated from represents the environmental pathways that the DHM would navigate through during the simulation. The graph = 〈 , , , , 〉 comprises a set of graph nodes and a set of edges . Each node ∈ represents free space in the environment, and has a position vector ( ) and cylinder attribute ( ), whose radius ( ) and height ℎ represent the distance to the wall and walkable step height, respectively. Each edge , representing the connectivity of free spaces, is generated between two adjacent nodes with a common region. = { } represents a set of stair edges connecting two graph nodes at the end of stairs. and can be generated automatically using our method [22]. By contrast, represents a 3D mesh model with high-quality textures, and it is used to estimate signage visibility and noticeability during simulation. can be created automatically using SfM with a set of photographs of the environment [24]. Detailed algorithms and demonstrations are given in our previous studies [22–24]. (a) (b) (c) Figure 2. 3D as-is environment model: (a) Walk surface points ; (b) navigation graph ; (c) textured environmental geometry . 4. Creation of Signage Entity In the proposed scheme, the signage system is modeled as a set of signage entities = { }. Each signage entity = [ , ] consists of a 3D textured mesh model of the signage and a set of signage information entities = { , } ( ∈ [1, ] ), where represents the number of signage information items included in . When modeling the existing signage, is constructed using SfM; otherwise, is created using 3D CAD software. , is created by manually assigning the geometric, navigation, and legibility properties in Table 2. The details are given below. 4.1. Geometric Property The geometric property includes the description region , center position , unit normal vector , width , and transformation matrix T . As shown in Figure 3a, = [ , ] consists of two diagonal points of the rectangular description region on , in which the signage information is written. , , and are estimated from . T represents a transformation matrix from the local coordinate system of , to the coordinate system of , where is defined to satisfy three conditions: (1) the origin of is located on , (2) y-axis of is aligned with , and (3) z-axis of is aligned with the z-axis of . Under this definition, T is calculated automatically from and . Figure 2. 3D as-is environment model: (a) Walk surface points WS; (b) navigation graph GN ; (c) textured environmental geometry GI . 4. Creation of Signage Entity In the proposed scheme, the signage system is modeled as a set of signage entities S = {Si}. Each signage entity Si = [Gi, Ii] consists of a 3D textured mesh model Gi of the signage and a set of signage information entities Ii = {Ii,j} (j ∈ [1, Ni]), where Ni represents the number of signage information items included in Si. When modeling the existing signage, Gi is constructed using SfM; otherwise, Gi is created using 3D CAD software. Ii,j is created by manually assigning the geometric, navigation, and legibility properties in Table 2. The details are given below. 4.1. Geometric Property The geometric property includes the description region Rg, center position pg, unit normal vector ng, width wg, and transformation matrix TG I . As shown in Figure 3a, Rg = [pto p, pbottom] consists of two diagonal points of the rectangular description region on Gi, in which the signage information is written. pg, ng, and wg are estimated from Rg. TG I represents a transformation matrix from the local coordinate system XI of Ii,j to the coordinate system XG of Gi, where XI is defined to satisfy three conditions: (1) the origin of XI is located on pg, (2) y-axis of XI is aligned with ng, and (3) z-axis of XI is aligned with the z-axis of XG . Under this definition, TG I is calculated automatically from pg and ng. ISPRS Int. J. Geo-Inf. 2017, 6, 267 6 of 22 Table 2. Signage information entity. Property Attribute Assignment Method Geometric property Description region Rg = [pto p, pbottom] Assigned by user by picking two diagonal points Center position pg Estimated from RgUnit normal vector ng Width wg Transformation matrix TG I Estimated from pg and ng Navigation property Type of signage Tn ∈ {′positional′, ′directional′, ′routing′, ′identi f ication′} Assigned by user based on the signage designName of indicated place Dn Navigation information NI Legibility property Maximum viewing distance dl Measured from human subjects Center point of 3D VCA pl Estimated from dlRadius of 3D VCA rl ISPRS Int. J. Geo-Inf. 2017, 6, 267 6 of 22 Table 2. Signage information entity. Property Attribute Assignment Method Geometric property Description region = [ , ] Assigned by user by picking two diagonal points Center position Estimated from Unit normal vector Width Transformation matrix T Estimated from and Navigation property Type of signage ∈{ , ′ , ′ , ′ ′} Assigned by user based on the signage design Name of indicated place Navigation information Legibility property Maximum viewing distance Measured from human subjects Center point of 3D VCA Estimated from Radius of 3D VCA (a) (b) (c) Figure 3. Overview of signage information: (a) Geometric property; (b) navigation property; (c) legibility property. 4.2. Navigation Property The navigation property includes the type of signage , name of indicated place , and navigation information . As listed and shown in Table 3 and Figure 3b, respectively, is Destination A (A) Positional sign ( is ) (B) Directional sign ( is ) (C) Routing sign ( is ) Destination A Destination A To Destination A (A) (B) (C) Figure 3. Overview of signage information: (a) Geometric property; (b) navigation property; (c) legibility property. 4.2. Navigation Property The navigation property includes the type of signage Tn, name of indicated place Dn, and navigation information NI . As listed and shown in Table 3 and Figure 3b, respectively, NI is assigned by the user in accordance with Tn. The user must specify a next goal position pn, next walking direction dn, and a set of passing points PN for positional, directional, and routing signage, respectively. pn and PN are specified w.r.t. the coordinate system XW of the textured environmental geometry GI . By contrast, dn is specified w.r.t. XI of Ii,j. ISPRS Int. J. Geo-Inf. 2017, 6, 267 7 of 22 Table 3. Assignment of navigation information depending on signage type. Signage Type Navigation Information NI to Achieve a Destination Referenced Coordinate System Positional signage Next goal position pn XW of GI Directional signage Next walking direction dn XI of Ii,j Routing signage A set of passing points PN = {pk} XW of GI Identification signage Name of current place Cn None 4.3. Legibility Property The legibility property includes the center point pl w.r.t. XI of Ii,j and radius rl of the 3D visibility catchment area (VCA). As shown in Figure 3c, the 3D VCA of signage represents a sphere in which people can recognize the information written in the signage. The VCA was defined originally as a 2D circle by Fillipidis et al. [25] and Xie et al. [26]. In this study, the 3D VCA is calculated such that the great circle of the sphere on the horizontal plane corresponds to the 2D VCA circle proposed by Xie et al. [26]. Specifically, pl and rl are calculated using the following equation: rl = wg 2 sin ϕl pl = pg + ng( wg 2 tan ϕl ) ϕl = tan −1 ( wg 2 dl ), (1) where dl represents the maximum viewing distance between the signage and the subject standing at a place, in which the subject can recognize the information on the signage. By measuring dl from the subjects, the legible space of the signage is calculated as the 3D VCA using Equation (1). 5. System for Evaluation of Ease of Wayfinding As shown in Figure 1, the wayfinding simulation using the DHM is performed in accordance with the user-specified wayfinding scenario, including DHM properties H = [M, θH , θV , nt], start position ps, initial walking direction dI , name of destination D, and signage locations and orientations Ts = {Ti}, where M, θH , θV , nt, and Ti represent motion-capture (MoCap) data for flat walking obtained from the gait database [27], horizontal and vertical angles of view frustum, threshold value of signage noticeability, and transformation matrix from XG to XW , respectively. Before the simulation, the locations and the orientations of each signage entity Si ∈ S are determined by assigning Ti ∈ Ts. Then, a DHM having the same body dimensions as the subject of M is generated. As shown in Figure 4, the DHM has 41 degrees of freedom and a link mechanism corresponding to that of M. The imitated eye position peye of the DHM is estimated as the midpoint between the top of the head and the neck. Finally, the wayfinding simulation is performed by repeating the algorithms described in the following subsections. ISPRS Int. J. Geo-Inf. 2017, 6, 267 8 of 22 ISPRS Int. J. Geo-Inf. 2017, 6, 267 8 of 22 Figure 4. Link mechanism of DHM. 5.1. Signage Perception Based on Imitated Visual Perception In the proposed system, signage visibility, noticeability, and legibility are estimated to determine whether a signage is found and its information is recognized by the DHM. The details are described in the following subsections. 5.1.1. Signage Visibility Estimation Signage visibility represents whether a signage is included in the view frustum of the DHM defined by and . As shown in Figure 5, it is estimated simply by scanning the eyesight of the DHM. First, the eyesight of the DHM is obtained using OpenGL by rendering an image from the camera model located at the DHM eye position . At the same time, as shown in the figure, the textured 3D environmental geometry and the textured 3D mesh model of each signage ∈ are rendered with a single color instead of their original textures. Finally, if the color of appears in the rendered image, is considered “visible” signage and inserted into a set of visible signage entities = { }. (a) (b) Figure 5. Signage visibility estimation: (a) View frustum of DHM; (b) image rendered using OpenGL. 5.1.2. Signage Noticeability Estimation As people overlook objects in their eyesight, it is not always true that the DHM can find a signage when is visible ∈ . Therefore, signage noticeability representing whether the DHM can notice ∈ must be estimated. Figure 4. Link mechanism of DHM. 5.1. Signage Perception Based on Imitated Visual Perception In the proposed system, signage visibility, noticeability, and legibility are estimated to determine whether a signage is found and its information is recognized by the DHM. The details are described in the following subsections. 5.1.1. Signage Visibility Estimation Signage visibility represents whether a signage is included in the view frustum of the DHM defined by θH and θV . As shown in Figure 5, it is estimated simply by scanning the eyesight of the DHM. First, the eyesight of the DHM is obtained using OpenGL by rendering an image from the camera model located at the DHM eye position peye. At the same time, as shown in the figure, the textured 3D environmental geometry GI and the textured 3D mesh model Gi of each signage Si ∈ S are rendered with a single color instead of their original textures. Finally, if the color of Gi appears in the rendered image, Si is considered “visible” signage and inserted into a set of visible signage entities Svis = {Sk}. ISPRS Int. J. Geo-Inf. 2017, 6, 267 8 of 22 Figure 4. Link mechanism of DHM. 5.1. Signage Perception Based on Imitated Visual Perception In the proposed system, signage visibility, noticeability, and legibility are estimated to determine whether a signage is found and its information is recognized by the DHM. The details are described in the following subsections. 5.1.1. Signage Visibility Estimation Signage visibility represents whether a signage is included in the view frustum of the DHM defined by and . As shown in Figure 5, it is estimated simply by scanning the eyesight of the DHM. First, the eyesight of the DHM is obtained using OpenGL by rendering an image from the camera model located at the DHM eye position . At the same time, as shown in the figure, the textured 3D environmental geometry and the textured 3D mesh model of each signage ∈ are rendered with a single color instead of their original textures. Finally, if the color of appears in the rendered image, is considered “visible” signage and inserted into a set of visible signage entities = { }. (a) (b) Figure 5. Signage visibility estimation: (a) View frustum of DHM; (b) image rendered using OpenGL. 5.1.2. Signage Noticeability Estimation As people overlook objects in their eyesight, it is not always true that the DHM can find a signage when is visible ∈ . Therefore, signage noticeability representing whether the DHM can notice ∈ must be estimated. Figure 5. Signage visibility estimation: (a) View frustum of DHM; (b) image rendered using OpenGL. 5.1.2. Signage Noticeability Estimation As people overlook objects in their eyesight, it is not always true that the DHM can find a signage Si when Si is visible Si ∈ Svis. Therefore, signage noticeability representing whether the DHM can notice Si ∈ Svis must be estimated. ISPRS Int. J. Geo-Inf. 2017, 6, 267 9 of 22 In the proposed system, signage noticeability is estimated using the saliency estimation algorithm proposed by Itti et al. [28] based on the visual search mechanism of real humans [29]. In this algorithm, a Gaussian pyramid is first generated from an image rendered by the camera model at peye. Then, feature maps representing contrasts of intensity, color differences, and orientations are obtained from each image. By integrating and normalizing the feature maps, a saliency map Ms = {m(x, y)} is generated, where m(x, y) ∈ [0, 1] represents the degree of saliency at a pixel (x, y). In the map, m(x, y) increases at the pixel, in which contrasts of intensity, color differences, and orientations are higher than those of other pixels. Finally, as shown in Figure 6, the propose system estimates the noticeability ni of visible signage Si ∈ Svis using the following equation: ni = max (x,y)∈Pi m(x, y), (2) where m(x, y) and Pi represent the degree of saliency at pixel (x, y) in MS and a set of pixels, in which the signage geometry Gi is rendered. If ni is greater than the noticeability threshold nt of the user-specified wayfinding scenario, Si is considered “found” signage, and inserted into a set of found signage entities S f ound = {Sk} (S f ound ⊆ Svis). ISPRS Int. J. Geo-Inf. 2017, 6, 267 9 of 22 In the proposed system, signage noticeability is estimated using the saliency estimation algorithm proposed by Itti et al. [28] based on the visual search mechanism of real humans [29]. In this algorithm, a Gaussian pyramid is first generated from an image rendered by the camera model at . Then, feature maps representing contrasts of intensity, color differences, and orientations are obtained from each image. By integrating and normalizing the feature maps, a saliency map = { ( , )} is generated, where ( , ) ∈ [0,1] represents the degree of saliency at a pixel ( , ). In the map, ( , ) increases at the pixel, in which contrasts of intensity, color differences, and orientations are higher than those of other pixels. Finally, as shown in Figure 6, the propose system estimates the noticeability of visible signage ∈ using the following equation: = max( , )∈ ( , ), (2) where ( , ) and represent the degree of saliency at pixel ( , ) in and a set of pixels, in which the signage geometry is rendered. If is greater than the noticeability threshold of the user-specified wayfinding scenario, is considered “found” signage, and inserted into a set of found signage entities = { } ( ⊆ ). Figure 6. Signage noticeability estimation. 5.1.3. Signage Legibility Estimation Signage legibility represents whether the DHM can recognize signage information of found signage ∈ , i.e., whether the DHM can read the textual or graphical information written on the signage. It is estimated using the 3D VCA of signage information , of . If is included in the 3D VCA of , , , is considered “recognized” signage information. In the proposed system, it is assumed that the DHM can correctly interpret , only when and , are found (i.e., ∈ ) and recognized, respectively. Note that the signage noticeability, , does not influence the signage legibility estimation. 5.2. Wayfinding Decision-Making Based on Signage Perception Based on the estimated signage visibility, noticeability, and legibility, the wayfinding state of the DHM is changed dynamically in accordance with the state transition chart shown in Figure 7a. As shown in the figure, when the simulation is performed, the DHM is set to start walking in the direction (state SW1 in Figure 7a). Then, as shown in Figure 7b, when a signage is found by the DHM, i.e., is inserted to , the DHM is set to walk toward the center position of , of (state SW2) to read the information on . Thereafter, the other signage does not influence the state transition until the state is changed to the look-around state (SW3) even if is found by the DHM. When , of becomes legible, the name of indicated place of , is compared with the name of destination of the wayfinding scenario. If , the state is changed to SW3 to find other signage related to ; else, the state is changed in accordance with the type of recognized signage information . If represents positional, directional, or routing signage, the state is changed to the motion planning state (SW4). By contrast, if represents an identification signage, the state is changed to the success state (SW5). In this state, the simulation is deemed complete because this state is the final state. Figure 6. Signage noticeability estimation. 5.1.3. Signage Legibility Estimation Signage legibility represents whether the DHM can recognize signage information of found signage Si ∈ S f ound, i.e., whether the DHM can read the textual or graphical information written on the signage. It is estimated using the 3D VCA of signage information Ii,j of Si. If peye is included in the 3D VCA of Ii,j, Ii,j is considered “recognized” signage information. In the proposed system, it is assumed that the DHM can correctly interpret Ii,j only when Si and Ii,j are found (i.e., Si ∈ S f ound) and recognized, respectively. Note that the signage noticeability, ni, does not influence the signage legibility estimation. 5.2. Wayfinding Decision-Making Based on Signage Perception Based on the estimated signage visibility, noticeability, and legibility, the wayfinding state of the DHM is changed dynamically in accordance with the state transition chart shown in Figure 7a. As shown in the figure, when the simulation is performed, the DHM is set to start walking in the direction dI (state SW1 in Figure 7a). Then, as shown in Figure 7b, when a signage Si is found by the DHM, i.e., Si is inserted to S f ound, the DHM is set to walk toward the center position pg of Ii, j of Si (state SW2) to read the information on Si. Thereafter, the other signage Sj does not influence the state transition until the state is changed to the look-around state (SW3) even if Sj is found by the DHM. When Ii, j of Si becomes legible, the name of indicated place Dn of Ii, j is compared with the name of destination D of the wayfinding scenario. If Dn 6= D, the state is changed to SW3 to find other signage related to D; else, the state is changed in accordance with the type of recognized signage information Tn. If Tn represents positional, directional, or routing signage, the state is changed to the motion planning state (SW4). By contrast, if Tn represents an identification signage, the state is ISPRS Int. J. Geo-Inf. 2017, 6, 267 10 of 22 changed to the success state (SW5). In this state, the simulation is deemed complete because this state is the final state. During the wayfinding simulation, the DHM basically repeats the states SW2, SW4, SW6, and SW3. As shown in Figure 7c, when the DHM recognizes Ii, j, it is set to walk toward the temporal destination of the DHM, i.e., subgoal position psub (SW4 and SW6). Then, as shown in Figure 7d, when the DHM arrives at psub, it is asked to observe the surrounding environment (i.e., look-around) by rotating the neck joint horizontally within its range of motion (SW3). When the DHM finds new signage in this state, the state changes back to SW2. By contrast, when the DHM cannot find any signage, the current DHM position is treated as a “disorientation spot” (SW7). The state SW7 is considered the failed state. Note that the state can be changed to SW8 from SW7 only when Tn represents a directional signage, as described in Section 5.3.1. ISPRS Int. J. Geo-Inf. 2017, 6, 267 10 of 22 During the wayfinding simulation, the DHM basically repeats the states SW2, SW4, SW6, and SW3. As shown in Figure 7c, when the DHM recognizes , , it is set to walk toward the temporal destination of the DHM, i.e., subgoal position (SW4 and SW6). Then, as shown in Figure 7d, when the DHM arrives at , it is asked to observe the surrounding environment (i.e., look-around) by rotating the neck joint horizontally within its range of motion (SW3). When the DHM finds new signage in this state, the state changes back to SW2. By contrast, when the DHM cannot find any signage, the current DHM position is treated as a “disorientation spot” (SW7). The state SW7 is considered the failed state. Note that the state can be changed to SW8 from SW7 only when represents a directional signage, as described in Section 5.3.1. (a) (b) (c) (d) Figure 7. Wayfinding decision-making based on signage perception: (a) Wayfinding state transition; (b) walking toward signage; (c) walking toward subgoal position; (d) look-around. 5.3. Signage-Based Motion Planning 5.3.1. Updating Subgoal Position of DHM In the signage-based motion planning state (SW4), first, the subgoal position is determined automatically depending on the type of recognized signage information and its navigation information . When = ′ ′, is determined as the next goal position of to make the DHM walk toward a location indicated by the recognized signage information , . When = ′ ′, as shown in Figure 8, a queue of fork points = { } is extracted by the following steps. (1) A graph node ( ∈ ) just under the pelvis position of the DHM is extracted from the navigation graph . Then, is inserted into a set of graph nodes , where , is found Walking trajectory Look-around Figure 7. Wayfinding decision-making based on signage perception: (a) Wayfinding state transition; (b) walking toward signage; (c) walking toward subgoal position; (d) look-around. 5.3. Signage-Based Motion Planning 5.3.1. Updating Subgoal Position of DHM In the signage-based motion planning state (SW4), first, the subgoal position psub is determined automatically depending on the type of recognized signage information Tn and its navigation information NI . When Tn =′ positional′, psub is determined as the next goal position pn of NI to make the DHM walk toward a location indicated by the recognized signage information Ii, j. When Tn =′ directional′, as shown in Figure 8, a queue of fork points F = {pm} is extracted by the following steps. ISPRS Int. J. Geo-Inf. 2017, 6, 267 11 of 22 (1) A graph node vc (vc ∈ V) just under the pelvis position pp of the DHM is extracted from the navigation graph GN . Then, vc is inserted into a set of graph nodes V′P, where V ′ P represents graph nodes on a feasible walking path when the DHM walks in accordance with the next walking direction dn indicated by Ii, j. (2) vc and dn of Ii, j are assigned to the variables vt and dt, respectively. (3) A graph node vp located in the direction of dt is extracted using the following equation: p = argmax k∈Nt dk·dt dk = t(vk)− t(vt) ‖t(vk)− t(vt)‖ , (3) where Nt represents a set of indices of graph node vk (vk /∈ V′P) connected to vt by a graph edge. Using this equation, vp is determined as a graph node with the minimum angle difference between dt and a graph edge connecting vk and vt. (4) If Nt 6= ∅, vp is inserted into V′P, and dk and vp are assigned to vt and dt, respectively. (5) If |Nt| ≥ 2 ∨ Nt = ∅, t(vp) is pushed into F because t(vp) is considered a center position at the fork way or at the terminal of the walkway. (6) Steps (3)–(5) are repeated, until Nt = ∅, i.e., until a graph node representing the terminal of the walkway is found. When the wayfinding state is changed to SW4 or SW8 in Figure 7a, a first fork point is taken from F and assigned to psub. This algorithm enables the proposed system to detect multiple disorientation spots, i.e., fork points with no visible and noticeable signage after perceiving directional signage. ISPRS Int. J. Geo-Inf. 2017, 6, 267 11 of 22 represents graph nodes on a feasible walking path when the DHM walks in accordance with the next walking direction indicated by , . (2) and of , are assigned to the variables and , respectively. (3) A graph node located in the direction of is extracted using the following equation: = arg max∈ ∙ = ( ) ( )‖ ( ) ( )‖, (3) where represents a set of indices of graph node ( ∉ ) connected to by a graph edge. Using this equation, is determined as a graph node with the minimum angle difference between and a graph edge connecting and . (4) If ∅, is inserted into , and and are assigned to and , respectively. (5) If | | 2 ∨ = ∅ , is pushed into because is considered a center position at the fork way or at the terminal of the walkway. (6) Steps (3)–(5) are repeated, until = ∅, i.e., until a graph node representing the terminal of the walkway is found. When the wayfinding state is changed to SW4 or SW8 in Figure 7a, a first fork point is taken from and assigned to . This algorithm enables the proposed system to detect multiple disorientation spots, i.e., fork points with no visible and noticeable signage after perceiving directional signage. Figure 8. Extraction of fork points from navigation graph. When = ′ ′, is determined as the last elements of a set of passing points of indicated by , . Then, the walking path of the DHM is estimated such that it passes the graph nodes at ∈ in Section 5.3.2. 5.3.2. Walking Path Selection and Walking Trajectory Generation As shown in Figure 9, after determining the subgoal position , the walking path = { } ( ∈ ) of the DHM is determined automatically by the following function: = Path( , ), (4) where Path( , ) represents a function to select a set of graph nodes between two nodes located at and using the Dijkstra method from . When the wayfinding state is changed to SW2 with the visible signage ∈ , ( ) and are assigned to and , where and represent a graph node just under the DHM pelvis position and the center position of , of , respectively. By contrast, when the state is changed to SW4, and are determined depending on the type of recognized signage information . When = ′ ′ or = ′ ′ , ( ) and are assigned to and , respectively. By contrast, when = ′ ′ , is determined as Figure 8. Extraction of fork points from navigation graph. When Tn =′ routing′, psub is determined as the last elements of a set of passing points PN of NI indicated by Ii, j. Then, the walking path VP of the DHM is estimated such that it passes the graph nodes at pk ∈ PN in Section 5.3.2. 5.3.2. Walking Path Selection and Walking Trajectory Generation As shown in Figure 9, after determining the subgoal position psub, the walking path VP = {vi} (vi ∈ V) of the DHM is determined automatically by the following function: VP = Path(pa, pb), (4) where Path(pa, pb) represents a function to select a set of graph nodes VP between two nodes located at pa and pb using the Dijkstra method from GN . When the wayfinding state is changed to SW2 with the visible signage Si ∈ Svis, t(vc) and pg are assigned to pa and pb, where vc and pg represent a graph node just under the DHM pelvis position pp and the center position pg of Ii, j of Si, respectively. By contrast, when the state is changed ISPRS Int. J. Geo-Inf. 2017, 6, 267 12 of 22 to SW4, pa and pb are determined depending on the type of recognized signage information Tn. When Tn =′ positional′ or Tn =′ directional′, t(vc) and psub are assigned to pa and pb, respectively. By contrast, when Tn =′ routing′, VP is determined as VP = ∪ k<|PN| k=0 Path(pk, pk+1), where pk ∈ PN is a passing point representing a walking route indicated by NI of Ii, j. After determining VP, the walking trajectory VT = 〈pi〉 is generated automatically by our previously developed optimization algorithm [23], where VT represents a sequence of sparsely discretized target pelvis positions of the DHM. This optimization algorithm is designed to make VT more natural and smooth, while avoiding contact with walls. The details are described in [23]. ISPRS Int. J. Geo-Inf. 2017, 6, 267 12 of 22 = ⋃ Path( , )| | , where ∈ is a passing point representing a walking route indicated by of , . After determining , the walking trajectory = 〈 〉 is generated automatically by our previously developed optimization algorithm [23], where represents a sequence of sparsely discretized target pelvis positions of the DHM. This optimization algorithm is designed to make more natural and smooth, while avoiding contact with walls. The details are described in [23]. Figure 9. Examples of walking path selection and walking trajectory generation. 5.4. MoCap-Based Adaptive Walking Motion Generation Finally, the walking motion of the DHM is generated as it follows using our MoCap-based adaptive walking motion generation algorithm [23]. In the algorithm, realistic articulated walking movements of the DHM are generated based on MoCap data for flat walking. The details and demonstrations are introduced in [23]. 6. Results and Validations The proposed system was developed using Visual Studio 2010 Professional edition with C++. The system was applied to a virtual maze and a real two-story indoor environment. In addition, it was validated by comparing the disorientation spots between the simulation and measurements obtained from young subjects. Videos of as-is environment modeling and wayfinding simulation results, i.e., Figures 10–13, are available in the supplementary video file. 6.1. Evaluation of Ease of Wayfinding in Virtual Maze The proposed system was first applied to a virtual maze with a set of signage entities = { , , , , }, to test its basic performance. Figure 10 shows the constructed environment model of the virtual maze. In the figure, textured environmental geometry was constructed manually using CAD software [30], and the set of walk surface points and navigation graph were constructed from a set of vertices of . Note that the proposed system could perform not only in the as-is environment model but in the given 3D model of the environment, e.g., CAD data of the environment, by converting the model to dense point clouds. Tables 4 and 5 show the wayfinding scenario and the user-assigned parameters of each signage information , , respectively. As shown in Table 5, all four types of signage were used. Figure 9. Examples of walking path selection and walking trajectory generation. 5.4. MoCap-Based Adaptive Walking Motion Generation Finally, the walking motion of the DHM is generated as it follows VT using our MoCap-based adaptive walking motion generation algorithm [23]. In the algorithm, realistic articulated walking movements of the DHM are generated based on MoCap data M for flat walking. The details and demonstrations are introduced in [23]. 6. Results and Validations The proposed system was developed using Visual Studio 2010 Professional edition with C++. The system was applied to a virtual maze and a real two-story indoor environment. In addition, it was validated by comparing the disorientation spots between the simulation and measurements obtained from young subjects. Videos of as-is environment modeling and wayfinding simulation results, i.e., Figures 10–13, are available in the supplementary video file. 6.1. Evaluation of Ease of Wayfinding in Virtual Maze The proposed system was first applied to a virtual maze with a set of signage entities S = {S1, S2, S3, S4, S5}, to test its basic performance. Figure 10 shows the constructed environment model of the virtual maze. In the figure, textured environmental geometry GI was constructed manually using CAD software [30], and the set of walk surface points WS and navigation graph GN were constructed from a set of vertices of GI . Note that the proposed system could perform not only in the as-is environment model but in the given 3D model of the environment, e.g., CAD data of the environment, by converting the model to dense point clouds. Tables 4 and 5 show the wayfinding scenario and the user-assigned parameters of each signage information Ii,j, respectively. As shown in Table 5, all four types of signage were used. ISPRS Int. J. Geo-Inf. 2017, 6, 267 13 of 22 ISPRS Int. J. Geo-Inf. 2017, 6, 267 13 of 22 (a) (b) (c) (d) Figure 10. Environment model of virtual maze: (a) Textured environmental geometry (#vertices: 4,241,573, #faces: 8,436,885); (b) walk surface points ; (c) navigation graph ; (d) wayfinding scenario. The results are available in the supplementary video file. Table 4. User-specified wayfinding scenario. Parameters Specified Values MoCap data for flat walking of MoCap data of a young male subject (Age: 22 years, height: 1.73 m) Horizontal angle of view frustum of 100 deg 1 Vertical angle of view frustum of 60 deg 1 Noticeability threshold ∈ [0, 1] of 0.3 2 Start position Shown in Figure 10d Initial walking direction Name of destination “Goal“ Signage locations and orientations Shown in Figure 10d 1 and were specified based on the handbook [31]. 2 was specified as a small value for validation. Table 5. User-assigned parameters of signage information. Parameters Sign Sign Sign Sign Sign Type of signage ‘Positional’ ‘Directional‘ ‘Directional‘ ‘Routing‘ ‘Identification‘ Name of indicated place “Goal“ Navigation information Shown in Figure 10d “Goal“ Maximum viewing distance 4.0 m 1 5.0 m 1 1.74 m 1 1 was specified as a tentative value without human measurements. Figure 10. Environment model of virtual maze: (a) Textured environmental geometry GI (#vertices: 4,241,573, #faces: 8,436,885); (b) walk surface points WS; (c) navigation graph GN ; (d) wayfinding scenario. The results are available in the supplementary video file. Table 4. User-specified wayfinding scenario. Parameters Specified Values MoCap data for flat walking M of H MoCap data of a young male subject (Age: 22 years, height: 1.73 m) Horizontal angle of view frustum θH of H 100 deg 1 Vertical angle of view frustum θV of H 60 deg 1 Noticeability threshold nt ∈ [0, 1] of H 0.3 2 Start position ps Shown in Figure 10d Initial walking direction dI Name of destination D “Goal“ Signage locations and orientations Ts Shown in Figure 10d 1 θH and θV were specified based on the handbook [31]. 2 nt was specified as a small value for validation. Table 5. User-assigned parameters of signage information. Parameters Sign S1 Sign S2 Sign S3 Sign S4 Sign S5 Type of signage Tn ‘Positional’ ‘Directional’ ‘Directional’ ‘Routing’ ‘Identification’ Name of indicated place Dn “Goal” Navigation information NI Shown in Figure 10d “Goal” Maximum viewing distance dl 4.0 m 1 5.0 m 1 1.74 m 1 1 dl was specified as a tentative value without human measurements. Figure 11 shows the evaluation results of ease of wayfinding. As shown in Figure 11a, when the simulation was performed, the DHM found and recognized S1 and I1, 1, respectively. In consequence, the DHM was set to walk toward the next goal positon pn indicated by I1, 1. Then, when the DHM arrived at pn, S2 and I2, 1 were found and recognized by the DHM (Figure 11b), respectively. A feasible ISPRS Int. J. Geo-Inf. 2017, 6, 267 14 of 22 walking path V′P and a set of fork points F of I2, 1 were then extracted. Then, the DHM was set to walk toward the first fork point p1 ∈ F of I2, 1. After that, the DHM found and recognized S3 and I3, 1 at p1 ∈ F, respectively. Then, as shown in Figure 11c, V ′ P and F of I3, 1 were extracted. At the same time, the DHM was set to walk toward p1 ∈ F of I3, 1. However, as shown in Figure 11d, the DHM could not find any new signage when it arrived at p1 ∈ F of I3, 1. Therefore, this spot was detected as a disorientation spot. As recommended by international standards [2], a facility manager must provide signage at all key decision points such as forks. Therefore, from this standpoint, the detection of this disorientation spot can be considered reasonable. ISPRS Int. J. Geo-Inf. 2017, 6, 267 14 of 22 Figure 11 shows the evaluation results of ease of wayfinding. As shown in Figure 11a, when the simulation was performed, the DHM found and recognized and , , respectively. In consequence, the DHM was set to walk toward the next goal positon indicated by , . Then, when the DHM arrived at , and , were found and recognized by the DHM (Figure 11b), respectively. A feasible walking path and a set of fork points of , were then extracted. Then, the DHM was set to walk toward the first fork point ∈ of , . After that, the DHM found and recognized and , at ∈ , respectively. Then, as shown in Figure 11c, and of , were extracted. At the same time, the DHM was set to walk toward ∈ of , . However, as shown in Figure 11d, the DHM could not find any new signage when it arrived at ∈ of , . Therefore, this spot was detected as a disorientation spot. As recommended by international standards [2], a facility manager must provide signage at all key decision points such as forks. Therefore, from this standpoint, the detection of this disorientation spot can be considered reasonable. (a) (b) (c) (d) (e) (f) Figure 11. Evaluation results of ease of wayfinding in virtual maze (red lines: graph edges, blue lines: graph edges on , cyan lines: , yellow lines: walking trajectory of DHM, purple lines: graph edges on ): (a) Wayfinding in accordance with ; (b) wayfinding in accordance with ; (c) wayfinding in accordance with ; (d) detecting disorientation spot; (e) wayfinding in accordance with ; (f) simulation was completed. The results are available in the supplementary video file. Figure 11. Evaluation results of ease of wayfinding in virtual maze (red lines: graph edges, blue lines: graph edges on VP, cyan lines: VT , yellow lines: walking trajectory of DHM, purple lines: graph edges on V′P): (a) Wayfinding in accordance with S1; (b) wayfinding in accordance with S2; (c) wayfinding in accordance with S3; (d) detecting disorientation spot; (e) wayfinding in accordance with S4; (f) simulation was completed. The results are available in the supplementary video file. ISPRS Int. J. Geo-Inf. 2017, 6, 267 15 of 22 Thereafter, as shown in Figure 11e, the DHM was set to walk toward p2 ∈ F indicated by I3, 1 to evaluate the ease of wayfinding after passing the detected disorientation spot. In consequence, the DHM found and recognized S4 and I4, 1 at p2 ∈ F of I3, 1, respectively. Then, the DHM was set to walk toward p4 ∈ PN of I4, 1 following VP generated on passing points pi ∈ PN of I4, 1. Finally, as shown in Figure 11f, the DHM found and recognized S5 and I5, 1, respectively, where S5 was an identification signage pertaining to the destination D. In consequence, the wayfinding simulation was completed. Based on the above results, from the standpoints of system performance, the following conclusions were obtained. • The proposed system could detect disorientation spots resulting from the lack of signage or poor location of signage in the environment model. • The proposed system could simulate the wayfinding of the DHM by discriminating among four types of signage, namely, positional, directional, routing, and identification. 6.2. Evaluation Results of Ease of Wayfinding in Real Two-Story Indoor Environment The proposed system was further applied to a real two-story indoor environment with a set of signage entities S = {S1, S2, S3, S4}. Figure 12 shows the constructed as-is environment model. In Figure 12, the laser-scanned point clouds were acquired from the environment by a terrestrial laser scanner [32]. The textured environmental geometry GI was constructed from 21,143 photos of the environment using commercial SfM software, ContextCapture [33], where the photos were extracted from the video data captured using a digital single-lens reflex camera [34]. As shown in Figure 12c, the model contains a few distorted regions, which can be attributed to the performance limitations of the SfM software. However, most of the model could be generated successfully. In the simulation, the DHM properties H of the wayfinding scenario was identical to that in Table 4. The starting position ps, initial walking direction dI , and signage locations and orientations TS are shown in Figure 12d,e. The maximum viewing distance dl of each signage was specified as dl = 4.46 m for each signage information Ii,j, as determined by measurement of dl of S1 using six subjects ranging in age from 22 to 26 years. A positional signage S1, two types of directional signage S2 and S3, and an identification signage S4 were arranged in the environment to simulate the situation in which people tried to find a conference room using only the signage in the unfamiliar indoor environment. Figure 13 shows the evaluation results of ease of wayfinding. As shown in Figure 13a, when the simulation was performed, S1 and I1, 1 were found and recognized by the DHM, respectively. Since the next goal position pn indicated by I1, 1 was specified on the end of the caracole on the second floor, the DHM was set to ascend the caracole. When the DHM arrived at pn of I1, 1, the DHM was asked to observe the surrounding environment to find new signage. However, as shown in Figure 13b, the DHM could not find S2 although S2 was visible. This was because the estimated signage noticeability n2 = 0.27 of S2 at the spot was less than the user-specified threshold, nt = 0.3. Thus, this spot was detected as a disorientation spot because S2 was overlooked. Following the above results, in Figure 13c, the signage design of S2, i.e., texture on Gi, was improved to enhance its noticeability. As a result, the ease of wayfinding was improved to enable the DHM to find S2 at the detected disorientation spot. This improvement was caused by the fact that n2 of S2 from the DHM standing at the disorientation spot detected previously increased to an adequately large value, n2 = 0.68. After the DHM recognized I2, 1, the DHM was set to walk toward the first fork point p1 indicated by I2, 1. However, as shown in Figure 13c, when the DHM arrived at p1 of I2, 1, the wayfinding state had fallen into SW7, i.e., gotten lost, since the DHM could not find any new signage at p1. This was because any signage could not be seen by the DHM at p1. Therefore, this spot was also detected as a disorientation spot owing to the lack of signage. ISPRS Int. J. Geo-Inf. 2017, 6, 267 16 of 22 ISPRS Int. J. Geo-Inf. 2017, 6, 267 16 of 22 (a) (b) (c) (d) (e) Figure 12. As-is environment model of two-story indoor environment: (a) Laser-scanned point clouds (#points: 5,980,647); (b) navigation graph ; (c) textured environmental geometry (#vertices: 625,484, #faces: 1,241,049); (d) wayfinding scenario on first floor [35]; (e) wayfinding scenario on second floor [35]. The results are available in the supplementary video file. Figure 12. As-is environment model of two-story indoor environment: (a) Laser-scanned point clouds (#points: 5,980,647); (b) navigation graph GN ; (c) textured environmental geometry GI (#vertices: 625,484, #faces: 1,241,049); (d) wayfinding scenario on first floor [35]; (e) wayfinding scenario on second floor [35]. The results are available in the supplementary video file. ISPRS Int. J. Geo-Inf. 2017, 6, 267 17 of 22 ISPRS Int. J. Geo-Inf. 2017, 6, 267 17 of 22 (a) (b) (c) (d) Figure 13. Evaluation results of ease of wayfinding in two-story indoor environment (yellow lines: walking trajectory of DHM): (a) Wayfinding simulation on first floor; (b) detection of disorientation spot resulting from overlooking the signage ; (c) design improvement of and detection of disorientation spot resulting from lack of signage; (d) ease of wayfinding improved completely by changing the design of and adding . The results are available in the supplementary video file. Figure 13. Evaluation results of ease of wayfinding in two-story indoor environment (yellow lines: walking trajectory of DHM): (a) Wayfinding simulation on first floor; (b) detection of disorientation spot resulting from overlooking the signage S2; (c) design improvement of S2 and detection of disorientation spot resulting from lack of signage; (d) ease of wayfinding improved completely by changing the design of S2 and adding S5. The results are available in the supplementary video file. ISPRS Int. J. Geo-Inf. 2017, 6, 267 18 of 22 By contrast, in Figure 13d, a new positional signage S5 was arranged around the detected disorientation spot. As a result, as shown in the figure, the wayfinding simulation of the DHM was completed successfully. As described above, the proposed system enabled the user to validate the ease of wayfinding in the environment interactively by considering the wayfinding of the DHM, as-is environment model, and arranged signage system. From the standpoint of system performance, the following conclusions were obtained. • The proposed system could detect disorientation spots resulting from the lack of signage and overlooking signage. • The proposed system could simulate the wayfinding of the DHM even in the realistic and complex as-is environment model. • The proposed system could quickly re-evaluate rearranged signage based on the simulation. 6.3. Efficiency of Environment Modeling and Simulation Table 6 shows the elapsed time of the as-is environment modeling and simulation. As shown in the table, the times for 3D environment modeling from laser-scanned point clouds were less than one minute in both environments. By contrast, owing to the performance limitation of the SfM software [33], construction of the textured environmental geometry GI required approximately one week. Table 6. Time required for environment modeling and simulation. (CPU: Intel(R) Core(TM) i7-6850K 3.60 GHz, RAM: 64 GB, GPU: GeForce GTX 1080). Process Time Required in Case of Virtual Maze Time Required in Case of Two-Story Indoor Environment Automatic construction of WS and GN from laser-scanned point clouds 2.5 s (#points: 963,691) 1 50.0 s (#points: 5,980,647) 1 Automatic construction of GI using SfM software [33] Approximately 1 week (#photos: 21,143) (resolution: 1920 × 1080) Signage visibility, legibility, and noticeability estimation Less than 0.17 s Signage-based motion planning Less than 0.02 s One-step walking motion generation with 100 frames interpolation 2 0.15 s 2.5 s 1 Number of downsampled points used for environment modeling. 2 Elapsed time of signage visibility, legibility, and noticeability evaluation was not included. Furthermore, the time required for signage visibility, legibility, and noticeability estimation was less than 0.17 s. In addition, the times required for one-step walking motion generation were 0.15 s and 2.5 s in the virtual maze and the two-story indoor environment, respectively. Therefore, it was confirmed that the proposed system could simulate the DHM wayfinding efficiently. Note that the time required for walking motion generation in the two-story indoor environment was longer owing to the high computational load of rendering the environment model. 6.4. Experimental Validation of System for Evaluating Ease of Wayfinding 6.4.1. Overview of Wayfinding Experiment The simulation results on ease of wayfinding presented in Section 6.2 were validated by the wayfinding experiment using six young subjects. In the validation, two signage systems imitating S = {S1, S2, S3, S4} and S ∪ S5 were arranged in the real environment, where S and S5 represent the set of signage entities used in the simulation in Figure 13a,b and the added signage in the simulation in Figure 13d, respectively. In the wayfinding experiment, first, the name of destination was revealed to the subjects at the start position ps. Then, the subjects were asked to find their way to the destination ISPRS Int. J. Geo-Inf. 2017, 6, 267 19 of 22 using the arranged signage system. During this process, wayfinding events such as finding signage and recognizing signage information were recorded by the thinking-aloud method [36], where the subjects were asked to walk while continuously thinking out loud. Verbal information from the subjects was recorded by handheld voice recorders. At the same time, videos of the walking trajectories of the subjects were captured by the observer. Finally, when the subjects arrived at the destination, the experiment was deemed complete. Note that all subjects have regularly used the environment, but the locations of arranged signage and the destination were not revealed to them. In addition, in the simulation results in Section 6.2, the maximum viewing distance dl was specified by measuring dl from those six subjects. In the experiments, first, the wayfinding behaviors of three young subjects (Y1–Y3) were measured using the signage system imitating S. After that, the behaviors of the other three young subjects (Y4–Y6) were measured using the signage system imitating S ∪ S5. 6.4.2. Comparison of Wayfinding Results between DHM and Subjects Figure 14 shows the comparison of wayfinding results between the DHM and the subjects. As shown in Figure 14a, a disorientation spot was found during the experiment by three subjects (Y1–Y3), which corresponded to the disorientation spot detected by the simulation. Thus, it was confirmed that the proposed ease of wayfinding simulation could detect disorientation spot, where the subjects actually lost their way owing to the lack of signage. ISPRS Int. J. Geo-Inf. 2017, 6, 267 19 of 22 in Figure 13d, respectively. In the wayfinding experiment, first, the name of destination was revealed to the subjects at the start position . Then, the subjects were asked to find their way to the destination using the arranged signage system. During this process, wayfinding events such as finding signage and recognizing signage information were recorded by the thinking-aloud method [36], where the subjects were asked to walk while continuously thinking out loud. Verbal information from the subjects was recorded by handheld voice recorders. At the same time, videos of the walking trajectories of the subjects were captured by the observer. Finally, when the subjects arrived at the destination, the experiment was deemed complete. Note that all subjects have regularly used the environment, but the locations of arranged signage and the destination were not revealed to them. In addition, in the simulation results in Section 6.2, the maximum viewing distance was specified by measuring from those six subjects. In the experiments, first, the wayfinding behaviors of three young subjects (Y1–Y3) were measured using the signage system imitating . After that, the behaviors of the other three young subjects (Y4–Y6) were measured using the signage system imitating ∪ . 6.4.2. Comparison of Wayfinding Results between DHM and Subjects Figure 14 shows the comparison of wayfinding results between the DHM and the subjects. As shown in Figure 14a, a disorientation spot was found during the experiment by three subjects (Y1–Y3), which corresponded to the disorientation spot detected by the simulation. Thus, it was confirmed that the proposed ease of wayfinding simulation could detect disorientation spot, where the subjects actually lost their way owing to the lack of signage. (a) (b) Figure 14. Comparison of wayfinding results between simulation and human measurements: (a) Comparison using = { , , , }; (b) comparison using ∪ . Figure 14. Comparison of wayfinding results between simulation and human measurements:(a) Comparison using S = {S1, S2, S3, S4}; (b) comparison using S ∪ S5. ISPRS Int. J. Geo-Inf. 2017, 6, 267 20 of 22 By contrast, as shown in Figure 14b, two subjects, Y4 and Y5, arrived at the destination when the signage system imitating S ∪ S5 was arranged. However, a disorientation spot was found during the experiment by subject Y6. This was explained by the fact that the subject Y6 overlooked the signage imitating S2. As shown in Figure 14a, this disorientation spot was also detected in the simulation because the DHM could not find S2 owing to the low noticeability of S2. Therefore, it was further confirmed that the proposed system could detect disorientation spot, where subjects actually lost their way owing to overlooking signage. 7. Conclusions In this study, we developed a simulation-based system for evaluating ease of wayfinding using a DHM in an as-is environment model. The proposed system was demonstrated using a virtual maze and a real two-story indoor environment. The following conclusions were drawn from our results: • Our system makes it possible to evaluate the ease of wayfinding by simulating the 3D interactions among the realistic wayfinding behaviors of a DHM, as-is environment model, and realistic signage system. • Under the user-specified wayfinding scenario, the system simulates the wayfinding of the DHM by evaluating signage locations, continuity, visibility, legibility, and noticeability based on the imitated visual perception of the DHM. • Realistic signage system, including four types of signage, namely, positional, directional, routing, and identification, can be discriminated in the wayfinding simulation. • Disorientation spots owing to the lack of signage and overlooking signage can be identified only by conducting the simulation. • Rearranged signage plans can be re-evaluated quickly by carrying out the simulation alone. Our system was further validated by comparison of disorientation spots between simulations and measurements obtained from six young subjects. From this validation, it was confirmed that the proposed system has a possibility of detecting disorientation spots, where people lose their way owing to the lack of signage or overlooking signage. To validate the performance of the proposed system in detail, wayfinding experiments with a greater number of subjects in various as-is environments, including outdoor environments, must be conducted using more complex wayfinding scenarios in a future work. Furthermore, in Sections 6.1 and 6.2, the noticeability threshold nt was specified without reference to measurements of human visual capabilities. However, in practice, nt must be specified as the minimum value estimated by the dominant users of the environment in consideration of their visual capabilities. Therefore, a method for determining a suitable value of nt using a statistical database related to human visual capabilities [37] will be developed in a future work. The textured environmental geometry GI of the two-story indoor environment included a few distorted regions owing to performance limitations of the SfM software and poor textures on the walls. In the proposed system, GI was used for signage noticeability estimation. From the standpoint of evaluating ease of wayfinding, the system must detect the disorientation spot, where low signage noticeability is expected. In general, the signage noticeability decreases in areas where wall surfaces around the signage are complex and textural, i.e., saliency of signage design is relatively low compared to its surroundings. Fortunately, in such areas, GI can be well reconstructed owing to the nature of the SfM algorithm. Therefore, the proposed system can detect disorientation spots resulting from overlooking signage, even if a part of GI is distorted. Furthermore, as mentioned in the literature [20], the presence of crowds influences the ease of wayfinding. Thus, crowd simulation technologies must be introduced into the proposed simulation framework. In addition, in the proposed system, the walking trajectory of the DHM was generated using a previously developed optimization algorithm [23]. However, as observed in Figure 14, the walking trajectories of individual human subjects vary. In our future work, such variabilities will ISPRS Int. J. Geo-Inf. 2017, 6, 267 21 of 22 be considered by introducing Monte Carlo simulation into the proposed system, i.e., generating a variety of DHM walking trajectories using the algorithm [23] with resampled parameters related to the trajectory generation. Supplementary Materials: The following is available online at www.mdpi.com/2220-9964/6/9/267/s1, Video S1: EvaluationResults.mp4. Acknowledgments: This work was supported by JSPS KAKENHI Grant No. 15J01552 and JSPS Grant-in-Aid for Challenging Exploratory Research under Project No.26560168. Author Contributions: Tsubasa Maruyama proposed the original idea of this paper; Tsubasa Maruyama developed the entire system and performed the experiments; Satoshi Kanai, Hiroaki Date, and Mitsunori Tada improved the idea of the paper; Tsubasa Maruyama wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. References 1. World Health Organization. WHO Global Report on Falls Prevention in Older Age. Available online: http://www.who.int/ageing/publications/Falls_prevention7March.pdf (accessed on 30 June 2017). 2. International Organization for Standardization. ISO21542: Building Construction—Accessibility and Usability of the Built Environment. Available online: https://www.iso.org/standard/50498.html (accessed on 15 December 2011). 3. International Organization for Standardization/International Electrotechnical Commission. ISO/IEC Guide 71 Second Edition: Guide for Addressing Accessibility in Standards. Available online: http://www.iec.ch/ webstore/freepubs/isoiecguide71%7Bed2.0%7Den.pdf (accessed on 1 December 2014). 4. Rubenstein, L.Z. Falls in Older People: Epidemiology, Risk Factors and Strategies for Prevention. Available online: https://www.ncbi.nlm.nih.gov/pubmed/16926202 (accessed on 22 June 2017). 5. Maruyama, T.; Kanai, S.; Date, H. Tripping risk evaluation system based on human behavior simulation in laser-scanned 3D as-is environments. J. Comput. Des. Eng. 2017, under review. 6. Churchill, A.; Dada, E.; de Barros, A.G.; Wirasinghe, S.C. Quantifying and validating measures of airport terminal wayfinding. J. Air Transp. Manag. 2008, 14, 151–158. [CrossRef] 7. Hunt, E.; Waller, D. Orientation and Wayfinding: A Review. Available online: http://citeseerx.ist.psu.edu/ viewdoc/summary?doi=10.1.1.46.5608 (accessed on 30 June 2017). 8. Hölscher, C.; Büchner, S.J.; Brosamle, M.; Meilinger, T.; Strube, G. Signs and maps: Cognitive economy in the use of external aids for indoor navigation. In Proceedings of the 29th Annual Conference of the Cognitive Science Society, Nashville, TE, USA, 1–4 August 2007. 9. Yasufuku, K.; Akizuki, Y.; Hokugo, A.; Takeuchi, Y.; Takashima, A.; Matsui, T.; Suzuki, H.; Pinheiro, A.T.K. Noticeability of illuminated route signs for tsunami evacuation. Fire Saf. J. 2017, in press. [CrossRef] 10. Thora, T.; Bergmann, E.; Konieczny, L. Wayfinding and description strategies in an unfamiliar complex building. In Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Boston, MA, USA, 20–23 July 2011. 11. Vilar, E.; Rebelo, F.; Noriega, P. Indoor human wayfinding performance using vertical and horizontal signage in virtual reality. Hum. Factors Ergon. Manuf. Serv. Ind. 2014, 24, 601–605. [CrossRef] 12. Buechner, S.J.; Wiener, J.; Hölscher, S. Methodological triangulation to assess sign placement. In Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara, CA, USA, 28–30 March 2012. 13. Furubayashi, S.; Yabuki, N.; Fukuda, T. A data model for checking directional signage at railway stations. In Proceedings of the First International Conference on Civil and Building Engineering Informatics, Tokyo, Japan, 7–8 November 2013. 14. Chen, Q.; de Vries, B.; Nivf, M.K. A wayfinding simulation based on architectural features in the virtual built environment. In Proceedings of the 2011 Summer Computer Simulation Conference, Hague, The Netherlands, 27–30 June 2011. 15. Morrow, E.; Mackenzie, I.; Nema, G.; Park, D. Evaluating three dimensional vision fields in pedestrian microsimulations. Transp. Res. Procedia 2014, 2, 436–441. [CrossRef] www.mdpi.com/2220-9964/6/9/267/s1 http://www.who.int/ageing/publications/Falls_prevention7March.pdf https://www.iso.org/standard/50498.html http://www.iec.ch/webstore/freepubs/isoiecguide71%7Bed2.0%7Den.pdf http://www.iec.ch/webstore/freepubs/isoiecguide71%7Bed2.0%7Den.pdf https://www.ncbi.nlm.nih.gov/pubmed/16926202 http://dx.doi.org/10.1016/j.jairtraman.2008.03.005 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.5608 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.46.5608 http://dx.doi.org/10.1016/j.firesaf.2017.04.038 http://dx.doi.org/10.1002/hfm.20503 http://dx.doi.org/10.1016/j.trpro.2014.09.053 ISPRS Int. J. Geo-Inf. 2017, 6, 267 22 of 22 16. Hajibabai, L.; Delavar, M.R.; Malek, M.R.; Frank, A.U. Agent-Based Simulation of Spatial Cognition and Wayfinding in Building Fire Emergency Evacuation. Available online: https://publik.tuwien.ac.at/files/ pub-geo_1946.pdf (accessed on 22 June 2017). 17. Brunnhuber, M.; Schrom-Feiertag, H.; Luksch, C.; Matyus, T.; Hesina, G. Bridging the gaps between visual exploration and agent-based pedestrian simulation in a virtual environment. In Proceedings of the 18th ACM Symposium on Virtual Reality Software and Technology, Toronto, ON, Canada, 10–12 December 2012. 18. Becker-Asano, C.; Ruzzoli, F.; Hölscher, C.; Nebel, B. A multi-agent system based on unity 4 for virtual perception and wayfinding. Transp. Res. Procedia 2014, 2, 452–455. [CrossRef] 19. Zhang, Z.; Jia, L.; Qin, Y. Optimal number and location planning of evacuation signage in public space. Saf. Sci. 2017, 91, 132–147. [CrossRef] 20. Motamedi, A.; Wang, Z.; Yabuki, N.; Fukuda, T.; Michikawa, T. Signage visibility analysis and optimization system using BIM-enabled virtual reality (VR) environments. Adv. Eng. Inf. 2017, 32, 248–262. [CrossRef] 21. Phaholthep, C.; Sawadsri, A.; Bunyasakseri, T. Evidence-based research on barriers and physical limitations in hospital public zones regarding the universal design approach. Asian Soc. Sci. 2017, 13, 133. [CrossRef] 22. Maruyama, T.; Kanai, S.; Date, H. Simulating a Walk of Digital Human Model Directly in Massive 3D Laser-Scanned Point Cloud of Indoor Environments. Available online: https://link.springer.com/chapter/ 10.1007/978-3-642-39182-8_43 (accessed on 22 June 2017). 23. Maruyama, T.; Kanai, S.; Date, H.; Tada, M. Motion-capture-based walking simulation of digital human adapted to laser-scanned 3D as-is environments for accessibility evaluation. J. Comput. Des. Eng. 2016, 3, 250–265. [CrossRef] 24. Maruyama, T.; Kanai, S.; Date, H. Vision-based wayfinding simulation of digital human model in three dimensional as-is environment models and its application to accessibility evaluation. In Proceedings of the International Design Engineering Technical Conferences & Computers & Information in Engineering Conference, Charlotte, NC, USA, 6–9 August 2016. 25. Filippidis, L.; Galea, E.R.; Gwynne, S.; Lawrence, P.J. Representing the influence of signage on evacuation behavior within an evacuation model. J. Fire Prot. Eng. 2006, 16, 37–73. [CrossRef] 26. Xie, H.; Filippidis, L.; Gwynne, S.; Galea, E.R.; Blackshields, D.; Lawrence, P.J. Signage legibility distances as a function of observation angle. J. Fire Prot. Eng. 2007, 17, 41–64. [CrossRef] 27. Kobayashi, Y.; Mochimaru, M. AIST Gait Database 2013. Available online: https://www.dh.aist.go.jp/ database/gait2013/ (accessed on 22 June 2017). 28. Itti, L.; Koch, C.; Niebur, E. A model of saliency-based visual-attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 1998, 20, 1254–1259. [CrossRef] 29. Treisman, A.M.; Gelade, G. A feature-integration theory of attention. Cogn. Psychol. 1980, 12, 97–136. [CrossRef] 30. FreeCAD: An Open-Source Parametric 3D CAD Modeler. Available online: https://www.freecadweb.org/ (accessed on 22 June 2017). 31. Takashi, T.; Keita, I.B. Physiology. In Handbook of Environmental Design, 2nd ed.; Koichi, I., Ed.; Maruzen Publishing: Tokyo, Japan, 2003. 32. 3D Laser-Scanner FARO. Available online: http://www.faro.com/products/3d-surveying/laserscanner- faro-focus/overview (accessed on 22 June 2017). 33. Bentley—Reality Modeling Software. Available online: https://www.bentley.com/en/products/brands/ contextcapture (accessed on 22 June 2017). 34. Nikon D3300. Available online: http://www.nikon-image.com/products/slr/lineup/d3300/ (accessed on 22 June 2017). 35. Floor Maps of Graduate School of Information Science and Technology. Available online: http://www.ist. hokudai.ac.jp/facilities/ (accessed on 22 June 2017). 36. O’Neill, M.J. Evaluation of a conceptual model of architectural legibility. Environ. Behav. 1991, 23, 259–284. [CrossRef] 37. Database of Sensory Characteristics of Older Persons with Disabilities. Available online: http://scdb.db.aist. go.jp/ (accessed on 22 June 2017). © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). https://publik.tuwien.ac.at/files/pub-geo_1946.pdf https://publik.tuwien.ac.at/files/pub-geo_1946.pdf http://dx.doi.org/10.1016/j.trpro.2014.09.059 http://dx.doi.org/10.1016/j.ssci.2016.07.021 http://dx.doi.org/10.1016/j.aei.2017.03.005 http://dx.doi.org/10.5539/ass.v13n4p133 https://link.springer.com/chapter/10.1007/978-3-642-39182-8_43 https://link.springer.com/chapter/10.1007/978-3-642-39182-8_43 http://dx.doi.org/10.1016/j.jcde.2016.03.001 http://dx.doi.org/10.1177/1042391506054298 http://dx.doi.org/10.1177/1042391507064025 https://www.dh.aist.go.jp/database/gait2013/ https://www.dh.aist.go.jp/database/gait2013/ http://dx.doi.org/10.1109/34.730558 http://dx.doi.org/10.1016/0010-0285(80)90005-5 https://www.freecadweb.org/ http://www.faro.com/products/3d-surveying/laserscanner-faro-focus/overview http://www.faro.com/products/3d-surveying/laserscanner-faro-focus/overview https://www.bentley.com/en/products/brands/contextcapture https://www.bentley.com/en/products/brands/contextcapture http://www.nikon-image.com/products/slr/lineup/d3300/ http://www.ist.hokudai.ac.jp/facilities/ http://www.ist.hokudai.ac.jp/facilities/ http://dx.doi.org/10.1177/0013916591233001 http://scdb.db.aist.go.jp/ http://scdb.db.aist.go.jp/ http://creativecommons.org/ http://creativecommons.org/licenses/by/4.0/. Introduction Related Work Automatic 3D As-Is Environment Modeling Creation of Signage Entity Geometric Property Navigation Property Legibility Property System for Evaluation of Ease of Wayfinding Signage Perception Based on Imitated Visual Perception Signage Visibility Estimation Signage Noticeability Estimation Signage Legibility Estimation Wayfinding Decision-Making Based on Signage Perception Signage-Based Motion Planning Updating Subgoal Position of DHM Walking Path Selection and Walking Trajectory Generation MoCap-Based Adaptive Walking Motion Generation Results and Validations Evaluation of Ease of Wayfinding in Virtual Maze Evaluation Results of Ease of Wayfinding in Real Two-Story Indoor Environment Efficiency of Environment Modeling and Simulation Experimental Validation of System for Evaluating Ease of Wayfinding Overview of Wayfinding Experiment Comparison of Wayfinding Results between DHM and Subjects Conclusions