DARGS: Dynamic AR Guiding System for Indoor Environments

Transcription

1 computers Article DARGS: Dynamic AR Guidg System for Indoor Environments Georg Gerstweiler *, Karl Platzer Hannes Kaufmann ID Institute Stware Technology Interactive Systems, Vienna University Technology, Favoritenstrasse /2, 1040 Vienna, Austria; (K.P.); (H.K.) * Correspondence: gerstweiler@ims.tuwien.ac.at; Tel.: +43-(1) Received: 21 November 2017; Accepted: 24 December 2017; Publhed: 28 December 2017 Abstract: Complex public buildgs, such as airports, use various systems guide people a certa destation. Such approaches are usually implemented by showg a floor plan that has guidg signs or color coded les on floor. With a technology that supports six degrees freedom (6DoF) trackg door environments, it possible guide people dividually, reby considerg obstacles, lengths, or ways for hicapped people. With an augmented reality (AR) device, such as a smart phone or AR glasses, can be presented on p real environment. In th paper, we present DARGS, an algorithm, which calculates a through a complex buildg real time. Usual planng algorithms use eir shortest s or dynamic s for robot teraction. human facr a real environment not considered. ma advantage DARGS corporation current field view (FOV) used device vualize a more dynamic presentation. Rar than searchg for AR content with a small FOV, with presented approach user always gets a meangful three-dimensional overlay dependent viewg direction. A detailed user study performed prove applicability system. results dicate that presented system especially helpful first few important seconds guidg process, when user still doriented. Keywords: augmented reality; door navigation; planng; vualization 1. Introduction Guidg people specific locations door environments a challengg task. Especially complex buildgs, such as airports, hospitals, or or public buildgs, operars are strugglg with problem guidg virs through ir buildg an optimized way. Virs ten have go through different s order reach ir dividual goals. refore, it necessary tailor a guidg system need a vitg person specific task that has be fulfilled. Th can be a cusmer, but also a person who needs fulfill matenance tasks or has deliver a package. Nowadays, guidg systems such as overhead signposts, maps, or digital stationary termals try help a person fd ir way. Individual solutions can only be provided with digital content, where current position user buildg needs be estimated. Many technologies have already been publhed [1] provg a reasonable trackg technique door environments with an accuracy a few meters down a few centimeters certa regions. Technologies usg signal strength triangulation methods allow us at least estimate a rough position. Or technologies, such as vion based methods [2], provide higher accuracy positional trackg also estimate orientation a device real time. However, re are still limitations concerng trackg volume reliability over time. While walkg through environment usg a camera a dplay a guidg setup, it possible present dividual augmented formation a user. Dplay devices, such as smartphones, Computers 2018, 7, 5; doi: /computers

2 Computers 2018, 7, Computers 2018, 7, While walkg through environment usg a camera a dplay a guidg setup, it possible present dividual augmented formation a user. Dplay devices, such as or smartphones, more sophticated or more devices, sophticated such asdevices, augmented such reality as augmented (AR) glasses reality such(ar) as glasses HoloLens, such are as able HoloLens, localize are person able while localize presentg person vual while content. presentg vual content. A number research projects focus on on implementg a novel a novel trackg technology, but but neglect neglect human human facr. facr. Pedestrians have have much much more more freedom freedom motion motion contrast contrast people people drivg drivg cars cars or or bicycles. refore, presentation guidg formation has be more flexible capable reactg sudden changes motion especially fast head movements. A prerequite have a reliable, valid, human understable calculated startg from current position user leadg wards a certa target. For that reason, paper at h presents a novel dynamic AR guidg system (DARGS). In th paper, focus lies on structure with a usually small AR field view (FOV) area which virtual content can always be observed by user. In order prevent user from searchg for relevant AR content (yellow Figure 1) with such a ty wdow virtual world, DARGS creates a new appearance a (green Figure 1). constructed dependent on a number hardware, human, environmental constrats, while leadg a person any three-dimensional (3D) position with door environment. Figure 1. green reactg viewg direction user contrast conventional s (yellow), which usually try calculate a direct. workat at h structured ur four sections. sections. Right Right after after dcussg dcussg or or approaches approaches th th area, area, proposed proposed system system presented. presented. It contas It contas design design system implementation details. A detailed description calculation given. Furrmore, solutions are presented enable an almost constant vible dependent viewg direction user. Before presentg technical results, a real-world AR guidg application will be described. Fally, a user study proves performance implemented algorithm with with participants, who who were were asked asked perform perform two two different different AR AR door door guidg guidg tasks. tasks. 2. Motivation Contribution While authors th work were dealg with various trackg solutions, cludg an implementation a vion-based trackg system, tested an airport scenario, lack available cusmizable generation algorithms became noticeable. Durg various tests, vualization for AR was not satfyg. Also, or approaches th direction only used fundamental graph traversal algorithms, such as A* [3,4] or Dijkstra [5], mostly order place static arrows along way. se basic approaches are not suitable not flexible enough create a credible AR guidg. Usg clearances walls far not enough provide user with constant guidg formation. Previous research has shown [2] [2] that that some some people do do not not need need guidg guidg formation all all time, time, but only but only at certa at certa spots. spots. For that For reason, that reason, re-itializations re-itializations are likely are likely be observed be observed along along.. user refore user refore always has always search has forsearch virtual for content virtual with content rar with small rar available small available wdow wdow virtual world. virtual world. FOV state FOV art state AR glasses art arear usually glasses around are usually 40 horizontal around 40 even horizontal less even vertical less dimension. vertical Because dimension. se Because reasons, we se believe reasons, thatwe a novel believe at plannga novel

3 Computers 2018, 7, algorithm, which specifically trimmed AR guidg tasks, has be developed would enhance guidg experience. In th paper, our ma contribution consts a well-thought-through planng algorithm trimmed for usage AR guidg applications. We provide a guidg an arbitrary 3D position an door environment real time on mobile devices dependg on size field view. In addition that, we out specific situations that appear when tegratg viewg direction current virtual field view calculation process. In contrast or work th area, th allows us present vual formation about direction all time, even if user lookg at an arbitrary viewg angle. Furrmore, we also present a decion-makg process based on a recalculation model for triggerg update. presented approach parameterizable, can be refore be adjusted available hardware performance. On p implemented, we designed a vualization dicatg direction speed. Neverless, it also possible test or vualizations with th, such as usg arrows or even walkg avatars. Occlusions are also considered at all times. presented approach designed be dependent trackg system can be tegrated any application where position orientation user known. implementation will be cluded DARGS, which contas calculation, a vualization, a guidg application on Microst HoloLens. Fally, we show that our suggested approach can successfully be used order guide pedestrians an door environment. A user study was conducted order observe how people behave when beg guided with AR glasses. We observed parameters such as walkg speeds, duration, amount formation needed order reach desired target. 3. Related Work We split related work two sections. First, projects dealg with implementation an door trackg are presented. se selected projects do not only present a trackg solution, but also use a cusm implementation a guidg task order evaluate quality ir work. Different approaches were used implement such a virtual guide AR. Anor area research concentrates on design a user terface guide people AR, deals with step-by-step descriptions or audio structions. In second part, relevant work that has been done area calculation for crowd simulation animation presented, although se approaches have never been adapted an AR guidg task its special requirements Trackg for Indoor Navigation Path Vualization For providg up--date guidg formation a user, it essential know user s current position with buildg. Different types technologies can be used estimate pose user room. In area AR door navigation, two approaches are commonly used. On one side, re are sparse localization technologies, which trackg only available with certa key-s real environment. On or side, re are implementations that allow for contuous trackg, with available position orientation. Th enables full capabilities AR vualizations at arbitrary locations. Dependg on used technologies, different guidg possibilities have been presented by different research groups. With Key-Pot Localization, it possible defe certa s room where user able receive trackg formation. Th gives user access AR content on specific predefed locations buildg. Work that has been publhed th area, such as [6 9], uses different kds markers provide locational trackg. Early work, such as [8] or [9], was based on black white fiducial markers specifically placed environment. Later, scientts used libraries such as Vuforia [10], which allows m use images as markers. Th makes it possible tegrate vual features environment [6,7] without need place fiducial markers on walls or floors. refore, features such as company logos, door plates, or patgs can be tegrated trackg

4 Computers 2018, 7, system. Due restricted trackg capabilities, vualizations can only be placed with immediate vicity se markers. Mulloni et al. [10] focused on design user terface formation presented user. In addition arrows, y also present formation such as walkg structions, targetg step countg turn formation. Furrmore, vualization computation guidg was also not that relevant se scenarios. In most approaches, static arrows are used communicate direction wards target user. In order guide user, Kasprzak et al. [7] used, for example, small arrows right on p markers with a known position room. A solution with more accurate trackg reby havg more guidg flexibility was presented by Alnabhan et al. [1]. developed trackg system based on Wi-Fi triangulation provides a position every 3 s. In combation with th position, a compass used detect orientation user relation target. Based on th development, authors that work implemented a user terface which shows a 3D arrow at a fixed on screen. Th red arrow rotates dependg on relation between phone s orientation target position. Kim et al. [11] presented a concept with AR door navigation with a self-developed trackg algorithm. Although trackg based on markers so-called image sequence matchg, which would allow for a 3D presentation AR data, users were only guided with a two-dimensional (2D) miature map p left corner an AR dplay. For dplayg AR guidg formation at arbitrary positions with a buildg, contuous trackg beneficial. In th area, most already publhed work contas trackg solutions with less focus on planng guidg process. Great door trackg approaches have been publhed [12,13], but unfortunately did not present any application scenarios or guidg approaches at all. ma focus se publications on trackg accuracy buildgs, although se setups would be perfect for evaluatg various methods area planng guidg methods. Or projects with similar trackg possibilities have tegrated guidg scenarios [2,14,15]. Miyashita et al. [14], for example, used Ubense trackg system provide high quality position formation. Usg a tablet device, users were able explore a museum with guidg formation. In th case, a virtual character was used give spatially related formation. In order brg attention special s terest, virtual balloons were placed with environment, which can be seen as a first step vually guide attention (viewg direction) user wards a certa target. Regardg vual contuous trackg combation with a guidg system, work Rehman et al. [15] has be mentioned. In th work, Metaio SDK (a framework for SLAM trackg marker trackg) was used establh a trackg system based on SLAM (simultaneous localization mappg) an door environment. Th approach was extended with sensor fusion a pair Google Glasses were tracked order guide user a certa target. As a vual guidg object, an arrow tegrated environment was used for showg direction. In addition that, researchers also tegrated audio structions. A majority already mentioned approaches are more concentrated on developed trackg system have less focus on actual guidg concept. Independent trackg capabilities, showg an arrow front user most used approach. arrow usually only shows a direction a next spover, constg predefed positions. Just a few approaches, such as [11], make use a simple graph traversal approach, such as well-known A* or Dijkstra algorithm, which provide at least a contuous from position user wards target order estimate direction target. None mentioned approaches take narrow FOV AR devices consideration. Also, viewg direction has not been addressed guidg scenarios where static virtual objects, such as arrows, were placed environment.

5 Computers 2018, 7, Path Planng For guidance applications, algorithms have be considered that on one h deliver a short or even shortest on or h need fd a way target destation case a exts. Algorithms such as Dijkstra s [5], A* [3,4], or modifications se are first reference s. se algorithms alone can be used fd a rough target, but are far from optimal sce shortest between two s usually runs along walls very close corners. se are not natural walkg s pedestrians. Anor solution compute a between a start an end a graph-based approach, where nodes can be user-defed. Usg key-s, which are generated out markers, was done [10]. Alnabhan et al. [1], for example, use a reference system where system sres direction from each wards target. shortest n defed by calculatg number references between start end. For defition reference s, Alnabhan et al. combed m with measurement s trackg system. Th bds planng algorithm very tightly trackg system refore hard tegrate or systems. In contrast a graph-based solution, concept navigation meshes opens up more flexibility calculatg s. Snook et al. [3] troduced navigation meshes Different representations have been developed sce n order represent a walkable area with a 3D mesh. Vibility Voronoi diagram [16], for example, a hybrid between vibility graph Voronoi diagram polygons a plane. vibility graph on one h represents shortest between obstacles door environment. Voronoi diagram on or h able create a with maximum clearance obstacles. se properties can be very important an AR application. In favor runtime performance, some approaches separate two necessary steps generation navigation planng algorithm. Kallmann et al., for example, use a Delaunay triangulation put geometry, reby considerg a clearance value order generate a triangulated navigation mesh called Local Clearance Triangulation [17,18]. Geraerts et al. on or h presented Explicit Corridor Maps [19] based on a medial ax [20]. medial ax related a Voronoi diagram represents s that are equidtant two or more polygons. It represents maximum clearance obstacles walls environment. dadvantage usg medial axes that y have be calculated for each dividually at runtime. se approaches work on planar surfaces. A 3D environmental model split up multiple planar layers a separate navigation mesh generated for each layer. Th allows calculation s with a multry buildg. Similar work from Geraerts et al. [21], most above-mentioned algorithms are used eir do crowd simulation, robot guidance, or for avatars walk through a virtual environment. None algorithms had requirement vualizg as a guidg strument a real walkg AR application. For th reason, vibility human facrs were not considered at all. 4. Proposed System Creatg a navigation aid for door environments a challengg task, sce multiple components have work ger order achieve a seamless correct vualization for user. work at h concentrates on fact that AR glasses only provide a small wdow for dplayg AR content. So, ma challenge how adapt a startg from user s position leadg target a way where user always gets vual guidg formation, dependent her/h viewg direction. In addition that, all components necessary for navigation have be real time capable, order be able regter virtual content at correct position real world. For creation a cusm, we first need a correct 3D model environment order calculate walkable areas. Sce users may also look directly at a wall, 3D model must conta

6 Computers 2018, 7, floors, walls, opengs such as doors. To know where model walkable areas ext, scale model has match real-world scale. Th especially important if, for example, staircases need be cluded. Concerng fal, which presented user, more formation has be provided. current position orientation user buildg have be known an AR-usable quality. Almost any trackg system capable dog th, such as a SLAM approach or trackg systems based on RGB-D data (color images with depth data), can be used. Regardg used AR device, we do not dem any special requirements, such as processg power or FOV area, sce target work focuses on both low- high-quality devices. ma dtctive feature state art AR dplay devices size field view, which should at least be a known value for presented work order be able tegrate it calculation. Anor requirement that was defed by authors was modularity developed approach. Th makes it possible exchange dividual components, such as trackg system or AR dplay device. modularity th solution especially important, because different application areas also dem different technologies. It also important defe stards area door navigation, for example, as it done OGC Stard for Indoor Spatial Information [22]. A modular system such as DARGS can easily be tegrated such a specification System Design In th chapter, framework presented system described detail. concept DARGS can be divided three major modules that work ger durg guidg task order react real-time data. As mentioned before, DARGS was designed be able be used with different trackg technologies AR dplay devices. For that reason, a server-client architecture was chosen, reby keepg load requirements dplay device low. Figure 2 gives a rough overview proposed system. server charge two modules responsible for calculatg preparg data for user side. Module one, Navigation Toolbox, holds core functionalities system. It prepares 3D model environment for calculatg s contas ma functionality calculation. Navigation Toolbox also reacts special circumstances as a result motion user. To achieve an FOV-dependent, algorithm renders 3D model environment a depth texture projects FOV user th model. As a result, a smaller area navigation mesh used order prepare a position- orientation-specific representation. Th approach compiled a library, which tended be used various applications, based on game enges such as Unity 3D or Unreal. For th work, authors decided use Unity 3D game enge for tegratg Navigation Toolbox on server side control client module ( Guidg Application ). client runs wirelessly on an AR device such as Microst HoloLens. second central module servicg for loadg 3D model, it hles all parameters wireless communication via Wi-Fi AR device. It also responsible for receivg trackg data decion-makg regardg vualization. third module runs exclusively on client AR device. It a Unity 3D application responsible for receivg convertg formation an understable vual representation. In presented scenario with side out trackg, Guidg Application also responsible for sendg trackg formation server.

7 Navigation Toolbox also reacts special circumstances as a result motion user. To achieve an FOV-dependent, algorithm renders 3D model environment a depth texture projects FOV user th model. As a result, a smaller area navigation mesh used order prepare a position- orientation-specific representation. Th approach compiled a library, which tended be used various applications, Computers 2018, 7, based on game enges such as Unity 3D or Unreal. DARGS Navigation Toolbox (Recast) Navigation Mesh Path Corridor Calc Server Guidg Logic Unit (Unity3D) 3D Environment Path Recalculation Logic Current Position Current Orientation Target Position Dplay Device Guidg Application (Unity3D) Trackg Path Renderg Computers 2018, 7, Figure communication 2. Systemvia Overview Wi-Fi AR Dynamic device. Augmented It also responsible Reality (AR) for receivg Guidg System trackg (DARGS). data Figure 2. System Overview Dynamic Augmented Reality (AR) Guidg System (DARGS). decion-makg Server part onregardg left side vualization. responsible for calculation. client consts a light Server part on left side responsible for calculation. client consts a light weight weight implementation third module for runs an AR exclusively device maly on responsible client AR for device. providg It a trackg Unity 3D dataapplication implementation for an AR device maly responsible for providg trackg data renderg. renderg. responsible 3D: for three-dimensional. receivg convertg FOV: field view. formation an understable vual 3D: representation. three-dimensional. In presented FOV: field scenario view. with side out trackg, Guidg Application also responsible for sendg trackg formation server Implementation For th work, authors decided use Unity 3D game enge for tegratg Navigation 4.2. Implementation followg Toolbox chapter on describes server side functionality control client developed module algorithm, ( Guidg startg Application ). from 3D model client runs followg endg wirelessly with chapter on an describes implementation AR device functionality such as Microst vualization. developed HoloLens. algorithm, startg second from central module 3D model servicg endg with for loadg implementation 3D model, vualization. it hles all parameters wireless Navigation Toolbox Navigation Toolbox Navigation Toolbox, cludg, implemented C++ with Recast library [23]. It Navigation a olboxtoolbox, for cludg planng tasks, contas implemented fundamental C++ olswith for mesh generation Recast library planng [23]. It methods a olbox amongst for planng or thgs. tasks contas first step fundamental algorithm ols for mesh prepare generation 3D model environment planng methods for amongst calculation. or thgs. As put first for step 3D algorithm model, a triangulated prepare mesh 3D (see model environment for calculation. As put for 3D model, a triangulated mesh Figure 3a) required. With help Recast library, a rasterization step creates a voxel height (see Figure 3a) required. With help Recast library, a rasterization step creates a voxel field, where size each voxel has be chosen accordgly. A denser voxel field has a big impact height field, where size each voxel has be chosen accordgly. A denser voxel field has a big on impact calculation on calculation time alltime followg all followg steps. After steps. that, After dividual that, dividual voxels voxels are connected are connected spans, whichspans, are categorized which are categorized walkable areas walkable areas non-walkable non-walkable areas. se areas. areas se are areas n are converted n regions converted (convex regions polygons), (convex which polygons), consider which static consider spaces static static spaces obstacles static obstacles such as walls. such as As a result, walls. one connected As a result, or one multiple connected separated or multiple regions separated can be regions detected. can be In detected. next step, In next borders step, regions borders are simplified regions speed are simplified up furr speed processg up furr steps. processg Fally, steps. a navigation Fally, a navigation mesh created mesh by triangulatg created by results triangulatg lastresults step, which represents last step, which walkable represents areas walkable whole areas 3D model. Th can whole be 3D achieved model. with Th can be Recast achieved library. with Recast library. (a) Figure Figure 3. (a) 3. 3D(a) Model 3D Model an an door door environment with approximately m 2 mused 2 used as a asreference a reference put model system; shows walkable area th environment. put model system; shows walkable area th environment. proposed implemented on p navigation mesh. fal calculated composed three sub s (see Figure 4). first leads from user FOV area. Next, side FOV area calculated, fally area between FOV target used falize. Each calculation based on a corridor, which a sequence mesh polygons connectg startg position with target position. To obta th

8 Computers 2018, 7, proposed implemented on p navigation mesh. fal calculated composed three sub s (see Figure 4). first leads from user FOV area. Next, side FOV area calculated, fally area between FOV target used falize. Each calculation based on a corridor, which a sequence mesh polygons connectg startg position with target position. To obta th corridor a first step, graph searchg algorithm A* used with entire navigation mesh as put. graph itself consts all edge mids navigation mesh. Computers 2018, 7, Between User - FOV Full Nav Mesh Path Corridor Shortest Path Middle Path Inside FOV Depth Texture FOV Mesh generation Key Pots Estimation FOV Nav Mesh Path Corridor Shortest Path Middle Path Between FOV - Target Full Nav Mesh Path Corridor Shortest Path Middle Path Path Smoothg FOV Path Figure 4. calculation process side DARGS. composed three sub s Figure 4. calculation process side DARGS. composed three sub s coverg area between user FOV - side FOV area - between border FOV coverg area between user FOV - side FOV area - between border target. FOV target. A* provides A* a very a very short direct target; target; refore, refore, it can be it can used be defe used defe corridor. However, resultg resultg A* almost A* cuts almost edges cuts edges ten runs right tennext runs right a wall next (see a wall (see red red Figure Figure 5), 5), can refore can refore not be used not for be used guidg for application. guidg application. Clearance Clearance walls or obstacles already given due region calculation, which means that A* runs walls or obstacles already given due region calculation, which means that A* parallel wall at a certa dtance. Th dtance walls, however, must not be o wide, sce th runs parallel wall at a certa dtance. Th dtance walls, however, must not be o wide, space between defed as a non-walkable area. If th area chosen be o wide it has two sce negative th space effects between application defed area as at a h. non-walkable For one, it area. not If possible th area calculate chosen a be if o wide it hasstartg two negative effects outside walkable application mesh. area So, at if h. a person For one, stg it next not possible a wall, calculate a planng if startg terrupted. outside second sue walkable could accrue mesh. at small So, ifpassages a personsuch stg as doorways. nextif a wall, clearance planng chosen terrupted. be o wide, a second doorway sue could could separate accrue at two small sides passages two such different as doorways. not- clearanceregions chosen no be navigation o wide, apossible. doorway For could that reason, separate th clearance two sides cannot be two used different If connected not-connected shape ; regions refore, noit navigation was necessary possible. implement Foranor that reason, way th guide clearance cannot wards be used shape middle ; refore, room. To achieve it was necessary a more centered implement, authors anor designed way guide Middle Path concept. wards Startg from corridor, Middle Path algorithm processes each polygon wards middle room. To achieve a more centered, authors designed Middle Path concept. target successively. In each step, edge between two neighborg polygons used add a new Startg from corridor, Middle Path algorithm processes each polygon wards middle. on th edge can be chosen any desired relation. Th relation target fluences successively. on which In each side step, room edge between generated. two neighborg For application polygons area at used h, an add equal a new relation middle was chosen.. It was also necessary on th edge make can sure be chosen still any situated desired with relation. boundaries Th relation fluences on walkg whicharea. sidefigure 5 room illustrates yellow generated. based Foron A* application blue areas at result h, an equal relation Middle waspath chosen. calculation. It was also Sce necessary th step has make be performed sure each still run, situated was necessary with design boundaries a simple walkg but fast area. method Figure tweak 5 illustrates wards yellowa certa based area along A* route. blue As figure as result shows, Middle Path passes calculation. door Sce at th center step has n be drawn performed wards each center run, it was room. necessary concept design a simple but Middle fast method Path used tweak all three sub wards s as a described certa area before, along but not route. usg only As figure startg shows, endg s global. Instead, several key s along route side current passes door at center n drawn wards center room. concept FOV area are estimated, which are used apply Middle Path multiple times. Middle Path used all three sub s as described before, but not usg only startg endg s global. Instead, several key s along route side current FOV area are estimated, which are used apply Middle Path multiple times.

9 Computers 2018, 7, Computers 2018, 7, Figure Yellow le on p shows a straight based on A* algorithm. blue le below shows Middle Path draggg wards center center corridor. corridor. red red le le dicates dicates viewg viewg direction direction user. user. trackg system delivers position a viewg direction algorithm. In addition trackg system delivers a position a viewg direction algorithm. In addition that, horizontal vertical FOV used AR dplay have be known. aim that, horizontal vertical FOV used AR dplay have be known. aim optimize only part that can be seen by user. To achieve th, area optimize only part that can be seen by user. To achieve th, area 3D model, which user lookg at, has be estimated. To hle th area a separate 3D model, which user lookg at, has be estimated. To hle th area a separate way, way, it necessary construct a new navigation mesh, FOVNavMesh. Sce objects it necessary construct a new navigation mesh, FOVNavMesh. Sce objects environment environment such as walls or or obstacles can occlude parts floor area front user, such as walls or or obstacles can occlude parts floor area front user, it necessary it necessary shoot rays startg from virtual position wards viewg direction. shoot rays startg from virtual position wards viewg direction. refore, 3D model refore, 3D model rendered a depth texture. rays are n projected rendered a depth texture. rays are n projected environment accordg environment accordg horizontal vertical FOV AR device. A grid 64 by 64 rays horizontal vertical FOV AR device. A grid 64 by 64 rays with a common startg position with a common startg position (position camera) creates several hit s on 3D model. (position camera) creates several hit s on 3D model. se hit s are n clustered se hit s are n clustered three categories: wall s, far s, floor s (see three categories: wall s, far s, floor s (see Figure 6a). Only floor s are Figure 6a). Only floor s are used for furr processg at th stage. In order generate used for furr processg at th stage. In order generate FOVNavMesh, conur all FOVNavMesh, conur all floor s calculated with help Moore Neighbor floor s calculated with help Moore Neighbor tracg algorithm [24]. result tracg algorithm [24]. result th a sum pixels describg boundary around viewed th a sum pixels describg boundary around viewed area on floor. As a next step, area on floor. As a next step, it necessary reduce se s. Douglas Peucker it necessary reduce se s. Douglas Peucker algorithm [25,26] used reduce algorithm [25,26] used reduce floor pixels a mimum, sce it considers whole polyle floor pixels a mimum, sce it considers whole polyle fd a similar curve dependg on a fd a similar curve dependg on a maximum dtance between origal fal polyle. maximum dtance between origal fal polyle. As a result, only corner s rema, As a result, only corner s rema, y describe conurs FOV. Th data structure y describe conurs FOV. Th data structure n treated like a new put n treated like a new put navigation mesh generation results FOVNavMesh. navigation mesh generation results FOVNavMesh. From now on, two navigation meshes From now on, two navigation meshes are available, but only FOVNavNesh dynamically changes. are available, but only FOVNavNesh dynamically changes. ma advantage dividg planng se two navigation meshes that ma advantage dividg planng se two navigation meshes that calculation FOVNavMesh much faster due small size FOV contrast calculation FOVNavMesh much faster due small size FOV contrast whole buildg. In worst case fast head movements, every frame could trigger recalculation whole buildg. In worst case fast head movements, every frame could trigger a recalculation FOVNavMesh. advantage speed that th area dynamic obstacles can also be FOVNavMesh. advantage speed that th area dynamic obstacles can also be cluded, which would be impossible use for whole navigation mesh. Dynamic obstacles are cluded, which would be impossible use for whole navigation mesh. Dynamic obstacles are all real-world objects that are not modeled with 3D environment, such as people, furnhgs, all real-world objects that are not modeled with 3D environment, such as people, furnhgs, or or movg objects. se can be detected with an RGBD camera, for example, fed or or movg objects. se can be detected with an RGBD camera, for example, fed algorithm. Recast library uses circular dynamic obstacles (see Figure 6b), which can now be algorithm. Recast library uses circular dynamic obstacles (see Figure 6b), which can now be cluded FOVNavMesh without losg much performance. cluded FOVNavMesh without losg much performance. At th algorithm, a full available based on whole navigation mesh. In addition that, a by far smaller navigation mesh ready for tegration proposed. With FOV area, a number terest s have be estimated order make sure that it possible seamlessly connect with full make sure built up right direction. In addition that, it was also necessary make sure enough understable.

10 Computers 2018, 7, content generated side FOV area, so that user able make sense vualized formation. To achieve th, first a center FOV area calculated. refore, centroid each triangle FOVNavMesh summarized a center mesh. However, a test necessary before contug, sce any obstacle or concave shape FOVNavMesh could lead a center a non-walkable area. In that case, center moved over closed border Computers 2018, 7, mesh (a) Figure (a) (a) Illustration FOV FOV calculation process. White White s s are are hit s with global navigation mesh. Red s represent area outside a walkable area. -shaped plane dtance represents max length FOV. green le le represents FOV. Fal FOVNavMesh cludg two round obstacles. At th algorithm, a full available based on whole navigation mesh. In As a next step, entry exit s FOV area are defed. entry on one addition that, a by far smaller navigation mesh ready for tegration proposed. h set on tersection between FOV conur a le connectg center With FOV area, a number terest s have be estimated order make sure that it position user. On or h, exit dependent on arrangement FOV possible seamlessly connect with full make sure built up area obstacles. To calculate exit, a Middle Path calculated, startg with center right direction. In addition that, it was also necessary make sure enough understable with target as fal. first vertex outside FOV area marked. If re content generated side FOV area, so that user able make sense vualized no obstacle between marked center, tersection between se two formation. To achieve th, first a center FOV area calculated. refore, centroid conur FOV exit. In case an tersection with a wall, for example, exit each triangle FOVNavMesh summarized a center mesh. However, a set between marked last Middle Path still side FOV. test necessary before contug, sce any obstacle or concave shape FOVNavMesh could Durg design testg algorithm, special conditions were defed. se conditions lead a center a non-walkable area. In that case, center moved over closed had be detected treated separately durg process generation maly border mesh. fluence previously described terest s. As a next step, entry exit s FOV area are defed. entry on one 1. h set most on common tersection condition between exts iffov conur user lookg a le direction connectg center shortest (A*), position which leads user. directly On or target, h, not exit considerg dependent viewg on direction. arrangement In th case, FOV Middle area obstacles. Path calculated To calculate between exit, entry a Middle exit Path calculated, FOVNavMesh. startg with Th center n with combed target as with fal a Middle. Path calculation first vertex outside between exit FOV area marked. target If re no position obstacle between user marked entry center (see Figure, 7a). tersection between se two conur 2. FOV If user exit looks. away In case from an shortest tersection, with a wall, side for example, FOV does exit not start set at between entry marked near user, but last at center Middle Path still FOV side area wards FOV. exit. Th reby Durg prevents design creation a testg shape algorithm, a loop (see special Figure conditions 7b). Th calculation were defed. triggered se conditions due had angle be detected between viewg treated separately direction durg average process direction first generation five s maly shortest fluence. previously described terest s In case most common user lookg condition at aexts wall, where if user no navigation lookg mesh direction available, shortest authors designed (A*), which concept leads directly a Wall Path. target, Figurenot 7c considerg illustrates outcome viewg direction. th prciple. In th It case, necessary Middle Path generate calculated a connection between from entry user exit lookg at wards FOVNavMesh. a valid global Th. Three n combed s are defed with a Middle before Path Wall calculation Path canbetween contued exit target. At first target, where position center user ray depth entry texture, (see representg Figure 7a). center FOV, hits an obstacle calculated. 2. If Th user looks set as away from first shortest new,. Whenside projectg FOV thdoes not start floor, at ntry le near user, but at center FOV area wards exit. Th reby prevents creation a shape a loop (see Figure 7b). Th calculation triggered due angle between viewg direction average direction first five s shortest. 3. In case user lookg at a wall, where no navigation mesh available, authors designed

11 Computers 2018, 7, Computers 2018, 7, Computers 2018, 7, calculated. between Th projected set as first user s position new calculated.. When projectg tersection th between floor, calculated. th le le Th between edge set projected as first navigation mesh added user s new. as aposition When second calculated. projectg. th Atersection third floor, added between le between with th a clearance le projected dtance edge wall navigation user s also mesh position used added calculated. as a startg as a second tersection for a global. A between calculation. third th le added with edge a clearance navigation dtance mesh wall added as also a second used as a startg 4.. If for A third user a global already added close calculation. with a clearance dtance wall also used as a startg target, destation could be side FOV area. In th case, 4. If user for a global only already calculated close calculation. between target, entry destation could target. be side FOV area. In th case, If user fal situation only already calculated close appearsbetween target, when user entry destation walkg could wards target. be side FOV area. In th case, target FOV already 5. behd fal situation only calculated target position. appears between when If th situation user entry walkg detected wards target. by algorithm, target center FOV already 5. behd fal FOV area situation target usedposition. appears as a startg If when th situation user. walkg detected wards Additionally, by algorithm, target shows center FOV way already wards FOV behd user. area target used as position. a startg If th situation. detected Additionally, by algorithm, shows center way wards FOV user. area used as a startg. Additionally, shows way wards user. (a) (c) (a) (c) Figure Figure7. 7. (a) (a) Representation Representation most most common commonsituation situation without without smoothg; smoothg; Situation Situation which Figure 7. (a) user Representation lookg away from most common shortest situation (yellow); without green smoothg; calculated. Situation (c) which user lookg away from shortest (yellow); green calculated. Illustration which user Wall lookg Path away concept. from shortest first set (yellow); most green center calculated. FOV (c) (c) Illustration Wall Path concept. first set most center FOV hittg Illustration Wall Path concept. on first set most center set FOV hittg wall. Second : closest on navigation mesh. Third : set save dtance from a wall. Second : closest on navigation mesh. Third : set save dtance from navigation mesh for smoothg process. navigation mesh for smoothg process. In usual situation, fal merged out three Middle Paths (see Figure 8a). before In a usual FOV, situation, side fal FOV, merged out three after Middle FOV. Paths (see whole Figure 8a). always calculated, before although FOV, user side only sees FOV, content FOV after area. FOV. reason for whole that that always only sees FOV area. reason for that that can can calculated, stay valid although over a longer user period only sees time. content As long as enough FOV area. formation reason present for that side that FOV stay valid over a longer period time. As long as enough formation present side FOV area, area, can stay no recalculation valid over a longer necessary period time. old As long stays as enough valid. formation present side FOV no area, recalculation no recalculation necessary necessary old old stays valid. stays valid. (a) (a) Figure 8. (a) Illustratg three areas calculation process. small rectangle middle describes Figure 8. (a) (a) Illustratg area between three areas user FOV. calculation green process. rectangle on small right rectangle represents middle FOV area describes ( user area lookg between away user from target). FOV. big green red rectangle on shows right represents area between FOV area FOV ( user user lookg lookg target. away away from from recalculation target). target). process big big red red rectangle considers rectangle shows shows number area les area between between sight FOV FOV s target. (yellow target. les) relation recalculation recalculation process number process considers considers not seen number number s les (outside les sight FOV sight or occluded s by (yellow obstacles) (yellow les) with les) relation a certa relation area. number number not seen not seen s (outside s FOV (outside or occluded FOV or by occluded obstacles) by obstacles) with a certa with area. a certa After fusg all sub s, a fal smoothg step performed. We used an algorithm related corridor After fusg map method all sub s, Geraerts a fal et smoothg al. [19]. As step a first performed. step, a backbone We used an algorithm calculated, related which consts corridor map three method sub s Geraerts put ger. et al. [19]. Additional As a first ways step, a backbone are added at constant calculated, dtances which consts three sub s put ger. Additional ways are added at constant dtances

12 Computers 2018, 7, After fusg all sub s, a fal smoothg step performed. We used an algorithm related corridor map method Geraerts et al. [19]. As a first step, a backbone calculated, which Computers 2018, 7, consts three sub s put ger. Additional ways are added at constant dtances along. For every, dtance closest obstacle calculated. An An attraction used, used, which which at at same same dtance away. away. By By calculatg a force a force velocity, a new a new smood added added now now fal fal.. Figure Figure 9 shows 9 shows overall procedure overall procedure executed at executed runtimeat explag runtime explag concept concept calculation calculation as well as as WallPath well as calculation. WallPath calculation. Figure Pseudocode describg calculation procedure WallPath Guidg Guidg All above-described logic hled side library, which meant be connected All above-described logic hled side library, which meant be connected any game enge for furr vualization. guidg part presented approach any game enge for furr vualization. guidg part presented approach implemented implemented Unity 3D game enge, which reby acts as a server unit on a stationary Unity 3D game enge, which reby acts as a server unit on a stationary personal computer personal computer (PC) or notebook as a client application runng on an AR device. In (PC) or notebook as a client application runng on an AR device. In course th work, we course th work, we made use Microst HoloLens as a client AR device. made use Microst HoloLens as a client AR device. By usg global itializg client device 3D model for HoloLens, it was By usg a global itializg client device 3D model for HoloLens, it was possible align real-world coordate system (HoloLens) with coordate system 3D possible align real-world coordate system (HoloLens) with coordate system 3D model environment runng on server. From that itialization, HoloLens tracks its own model environment runng on server. From that itialization, HoloLens tracks its own pose through environment. In periodic tervals, which can be set between 5 30 updates per pose through environment. In periodic tervals, which can be set between 5 30 updates second, new pose HoloLens sent server. In th case, real environment can be per second, new pose HoloLens sent server. In th case, real environment can be simulated on server part with 3D model environment without need for transferrg simulated on server part with 3D model environment without need for transferrg reconstructed mesh HoloLens. reconstructed mesh HoloLens. server part responsible for decion-makg if new has be calculated or old server part responsible for decion-makg if a new has be calculated or old still vible for user. refore, recalculation model was implemented Unity 3D, still vible for user. refore, a recalculation model was implemented Unity 3D, which monirs length still located current FOV area. To achieve th, round which monirs length still located current FOV area. To achieve th, a round area defed around current user position dependent current FOV. amount area defed around current user position dependent current FOV. amount s with th area counted. Th number has be compared number s currently seen by user. An area accordg horizontal vertical FOV device spanned front user. By creatg vecrs between device position each, it possible decide wher a with FOV or not by calculatg dot product. In addition that, it necessary do a raycast wards every, sce obstacles room

13 Computers 2018, 7, could easily occlude high numbers s. Figure 8b illustrates th concept. ratio between all s with a radius vible s decide wher a recalculation necessary or not. In th way, it could be that only one has be created for entire navigation task, but only if Computers 2018, 7, user not losg out sight. Unity 3D application runng on AR device receives a new as soon as it changed on server side. s On with p th area, counted. a vualization Th number was has implemented. be compared In a navigation number task direction s essential, currently which seen by has user. be communicated An area accordg user. In horizontal course th vertical work, FOV we focus on device a pure vual spanned concept front without user. textual Byformation creatg vecrs audio between support. device In addition position that, each requirement, it a possible low calculation decideperformance wher a a with good understability FOV or not by calculatg necessary. dot product. In addition that, For itthat necessary reason, a particle do a raycast was wards implemented, every which, consts sce obstacles colored s roomflowg could easily from occlude start high numbers wards s. target Figure dependent 8b illustrates on speed th concept. user. Sce ratioit between possible all s recalculate with a radius several vible times, at s each decide way wher particles a recalculation are created necessary immediately. or not. Figure In th way, 12c shows it coulda screenshot be that only one a user observg has be created vualization for entire through navigation HoloLens. task, but only if user not losg out sight. Unity 3D application runng on AR device receives a new as soon 5. asresults it changed on server side. On p proposed th, system was a vualization evaluated was two implemented. stages. first In apart navigation describes taska pure direction technical evaluation essential, which targetg has runtime be communicated performance various user. situations, In course reby th identifyg work, weparameters focus on a with pure great vualimpact concept on without performance. textual formation In a second or audio stage, support. DARGS In was addition set up that, a real environment requirementfor a user low calculation evaluation, performance where each user a good was understability given two different necessary. navigation tasks through a 200 m 2 environment. For that reason, technical a particle evaluations waswere implemented, performed which on a consts Schenker XMG colored notebook s flowg with an from Intel i startcpu, 16.0 wards GB RAM, target a dependent GeForece GTX880M. on speed user user. study Sce used it addition possible recalculate Microst Hololens, several a Wi-Fi times, router, at each way HoloLens clicker particles put are device created for immediately. simple user teraction. Figure 12c shows a screenshot a user observg vualization through HoloLens Technical Evaluation 5. Results For technical evaluation, two different 3D models door environments were used. Figure 10a shows a 2500 proposed m system was evaluated two stages. first part describes a pure technical 2 environment with about vertices, whereas Figure 10b illustrates a 700 m 2 environment evaluation targetg with only runtime performance vertices. calculation various situations, whole reby navigation identifyg mesh parameters takes several with seconds great impact up on several performance. mutes for In a second whole stage, buildg, DARGS was dependent set up a on real environment cell size used. for a Sce user th evaluation, has only where be each done user once was an given fle two process, different it navigation not dcussed tasks through detail. a 200 m 2 environment. technical In followg, evaluations we focus were on performed algorithm on a Schenker executed XMG durg notebook runtime. with Especially, an Intel two i facrs CPU, 16.0 process GB RAM, a GeForece calculation GTX880M. can fluence user study runtime. used First, addition size Microst FOV Hololens, area, second, a Wi-Fi router, length HoloLens Table clicker 1 shows put device all for necessary simple user steps teraction. that have be performed durg runtime that are dependent on FOV size length. left side table uses a fixed 5.1. Technical Evaluation length a variable FOV size 20 m m 2. FOVNavMesh generation a limitg facr For technical algorithm. evaluation, With a calculation two different time 3D models 262 ms at door 120 m 2, environments it far from were real-time used. performance. Figure 10a shows A reasonable a 2500 m 2 FOV environment size terms with about calculation vertices, time whereas vualization Figurecan 10bbe illustrates seen Figure a 700 m10a. 2 environment With about with a 20 only m 2 FOV 1.000size, vertices. a reasonable calculation calculation time whole 100 navigation ms per meshcan takes be guaranteed. several seconds To reduce up several size mutes FOV fordurg whole runtime, buildg, algorithm dependent able on limit cellmaximum size used. dtance Sce th has only hit s be done once depth an texture, fle reby process, decreasg it not dcussed size detail. used FOVarea. (a) Figure Figure Two Two possible possible outcomes outcomes (a,b) (a,b) a navigation a navigation mesh mesh used used technical technical evaluation. evaluation. (a) Environment (a) Environment used used user user study. study. Environment Environment with with multiple multiple corridors. corridors. In followg, we focus on algorithm executed durg runtime. Especially, two facrs process calculation can fluence runtime. First, size FOV area,

14 Computers 2018, 7, second, length Table 1 shows all necessary steps that have be performed durg runtime that are dependent on FOV size length. left side table uses a fixed length a variable FOV size 20 m m 2. FOVNavMesh generation a limitg facr algorithm. With a calculation time 262 ms at 120 m 2, it far from real-time performance. A reasonable FOV size terms calculation time vualization can be seen Figure 10a. With about a 20 m 2 FOV size, a reasonable calculation time 100 ms per can be guaranteed. To reduce size FOV durg runtime, algorithm able limit maximum dtance hit s depth texture, reby decreasg size used FOVarea. Table 1. A performance comparon all parts with calculation that are runng durg guidg process. two variables varyg durg th process are analyzed: length size FOV. Stage Algorithm Path Length 50 m FOV Size 10 m 2 FOV Size 20 m 2 FOV Size 120 m 2 Path Length 50 m Path Length 160 m Path Corridor 0.05 ms 0.05 ms 0.06 ms 0.20 ms Middle Path 0.02 ms 0.02 ms 0.03 ms 0.05 ms Render FOV 22.2 ms 23.8 ms 22.1 ms 22.2 ms Filter Floor Pixels 0.27 ms 0.09 ms 0.12 ms 0.1 ms FOV NavMesh 44.8 ms ms 25.0 ms 26.6 ms FOV Path 0.07 ms 0.05 ms 0.03 ms 0.04 ms Smooth FOV 0.24 ms 2.1 ms 0.4 ms 0.5 ms Global Path 0.10 ms 0.08 ms 0.12 ms 0.4 ms Global Smoothg 19.4 ms 14.6 ms 17.9 ms ms Full Runtime 92.0 ms ms 70.5 ms ms second variable fluencg speed calculation length. refore, a length m compared Table 1. It obvious that calculation time smoothg process highly fluenced by length. Dependg on desired calculation effort, it possible limit smoothg process a certa dtance sce user not able see whole at once. Figure 10b plots overall calculation time dependg on length. With about an 80 m length, calculation time measured with 100 ms. With workg region about a 10 m 2 40 m 2 FOV a reasonable global length 20 m 100 m, calculation time creases a lear way for both facrs. In order limit whole calculation time, a balance between size FOV area global length smoothg process can be found. In a usual situation with an FOV size 10 m 2 a smood length 50 m, about 14 s per second can be provided for dplay device. With algorithm, parameters maxpathlength maxfovsize can be adjusted dependg available performance system. How ten it necessary recalculate was tested amongst or thgs real-world scenario with 16 participants User Study ma goal user study was fd out if a user can be guided with developed dynamic AR through a real environment. hypos if an FOV-dependent calculation facilitates way fdg process door environments has be answered. Furrmore, study should show if a user needs navigational formation all time or not. With user study, first users had accomplh multiple guidg tasks while wearg Microst HoloLens. After that, a detailed questionnaire was filled, contag questions concerng tasks general question about concept. questionnaire based on seven- Likert scale [27]. Fally, stardized system usability scale (SUS) [28] used rate whole system. user study consted two guidg tasks. With first task, users were placed a 200 m 2 environment. Each participant had follow two different types through th environment (see Figure 11a). Half users started with followed by A* or

15 Computers 2018, 7, Computers 2018, 7, half participants performed task or way round. In each run, five different targets (see Figure 11b) with same overall dtance had be found after a short troduction. To guarantee that user saw virtual target real environment, y had write down a code written on target. In addition that, each user was holdg HoloLens Clicker was structed press butn if should be shown. vualization for both sub tasks illustrated Figure 12c. Th task was performed by 16 participants, 4 females 12 males, between age 24 Computers 2018, 7, For eight participants, (a) it was ir first time usg an AR device. Figure 11. (a) Graph showg calculation time relation size FOV area. length fixed 50 m. Illustration calculation relation size global length. FOV area fixed 10 m 2. All participants were able fd all targets placed environment with as well as with A*. question about if vualized was tuitive follow was answered with a mean 6.37 (with σ = 0.80), an almost perfect result. constant changes, due head rotations, were no problem for a majority participants, as y got used immediately dicated that it tuitive follow. Sce every user could decide with a click a butn at which time have vualized, it was possible ask if people (a) would rar see all time, or just at certa s. Four participants out 16 would prefer see only on dem. Overall, th answer resulted Figure Figure (a) (a) Graph Graph showg showg calculation calculation time time relation relation size size FOV FOV a mean value 4.8 (with σ = 2.28) (see Figure 12a), which tells us that majority users would area. area. length length fixed fixed m. m. Illustration Illustration calculation calculation relation relation size size like see all time. Th would mean that a user has multiple startg s between global global length. length. FOV FOV area area fixed fixed m 2 m. 2. start position target, each time not knowg where would be shown. All participants were able fd all targets placed environment with as well as with A*. question about if vualized was tuitive follow was answered with a mean 6.37 (with σ = 0.80), an almost perfect result. constant changes, due head rotations, were no problem for a majority participants, as y got used immediately dicated that it tuitive follow. Sce every user could decide with a click a butn at which time have vualized, it was possible ask if people would rar see all time, or just at certa s. Four participants out 16 would prefer see only on dem. Overall, th answer resulted a mean value 4.8 (with σ = 2.28) (see Figure 12a), which tells us that majority users would like see (a) all time. Th would mean that a user has multiple startg s (c) between start Figure position 12. (a) Area target, 160 m² each used time scenario not knowg one contag where five green targets would be user shown. Figure 12. (a) Area 160 m 2 used scenario one contag five green targets user had had reach. reach. Area Area m m² 2 used used for for second second scenario. scenario. big big green green circle circle represents represents position position user user stg. stg. small small circles circles are are targets targets room; room; (c) (c) A view view through through camera camera HoloLens HoloLens with with a vualized vualized.. After users fhed first scenario, a large number participants reported that All participants were able fd all targets placed environment with as difference between two runs mor was hardly recognized, which was also reflected well as with A*. question about if vualized was tuitive follow was results, for example question if participant would use one or or approach. answered with a mean 6.37 (with σ = 0.80), an almost perfect result. constant changes, difference mean value between se two questions was only 0.16 s. due head rotations, were no problem for a majority participants, as y got used duration first task was about 160 s with no measurable difference between two immediately dicated that it tuitive follow. calculations. Durg th (a) time, was calculated 377 times (median value), which (c) results Sce every user could decide with a click a butn at which time have vualized, one every 2.52 s. average walkg speed, cludg spovers for writg down code it was Figure possible 12. (a) ask Area if people 160 m² would used rar scenario seeone contag all five time, green or targets just at certa user had s. reach. Four on target, was also very similar with 1.98 km/h at A* 2.22 km/h with. participants Area out 6516 m² would used for prefer second seescenario. only big on green dem. circle represents Overall, th position answer resulted user a Sce results first task proved that can lead people arbitrary targets, mean value stg. 4.8 (with small σ circles = 2.28) are (see targets Figure 12a), room; which(c) tells A view us that through majority camera users HoloLens would like second guidg task was designed study advantage. Th maly ares at see with a vualized all time.. Th would mean that a user has multiple startg s between start begng a navigation task or when user aga askg for guidance formation after a position target, each time not knowg where would be shown. After users fhed first first scenario, a a large number participants reported that that difference between two runs mor was hardly recognized, which was also reflected results, for example question if participant would use one or or approach. difference mean value between se two questions was only 0.16 s. duration first task was about 160 s with no measurable difference between two calculations. Durg th time, was calculated 377 times (median value), which results one every 2.52 s. average walkg speed, cludg spovers for writg down code

16 Computers 2018, 7, results, for example question if participant would use one or or approach. difference mean value between se two questions was only 0.16 s. duration first task was about 160 s with no measurable difference between two calculations. Durg th time, was calculated 377 times (median value), which results one every 2.52 s. average walkg speed, cludg spovers for writg down code on target, was also very similar with 1.98 km/h at A* 2.22 km/h with. Sce results first task proved that can lead people arbitrary targets, Computers 2018, 7, second guidg task was designed study advantage. Th maly ares at while. begng Twelve participants a navigation (3 task female or when 9 male) user between aga askg ages for 24 guidance 50 formation participated after th a while. study. Twelve For th participants task, user (3had female stay 9 male) middle between a room ages (see Figure 24 11b) 50 participated lookg at a certa th study. direction. ForWithout th task, walkg useraround, had stay user had middle identify a room eight (see targets Figure 11b) room lookg located a around certa direction. user. Each Without user walkg was testg around, user had identify A* straight eight. targets Whenever room a target located was around vible user. user, Eacha user click was had testg be performed, which activated A* straight new. target Whenever a room targetuntil was vible fal target user, was a click reached. had be performed, which activated new target room until fal target was reached. In th second scenario, results dicate that contrast straight was rated Insignificantly th second better. scenario, Th can results be seen dicate Figure that12b contrast showg result straight followg question: was rated How significantly good could you better. anticipate Th can bedirection seen Figure 12b target? showg result was rated followg with a question: score How 6.08 (with goodσ could = 1.73) you anticipate contrast direction straight with target? a score 2.83 (with was σ = rated 1.74). with Also, a score time 6.08 for completg (with σ = 1.73) task contrast (see Figure 13) straight differs significantly with a score between 2.83 (with A*-based σ = 1.74). straight Also, time for completg. A median task (see Figure s 13) was differs measured significantly for completg between A*-based task with straight straight. contrast A23.32 median s for s was measured for completg task with straight contrast s for on click always with a click a butn Anticipation direc Target not at all all time Straight Path felt very confident usg needed learn a lot very cumbersome use learn use th system very quickly like use system frequently 5 (a) (c) o much constency DARGS Straight Path unnecessarily complex easy use need support a technical functions were well tegrated Figure Figure (a) (a) Bar Bar chart representg results results question question if if user user wants wants see see all all time time or or with with click click a butn (Scenario 1). 1). Results question how good target direction could be be anticipated with. (c) A breakdown results system usability scale (SUS) for dividual questions comparg both calculation concepts. In th scenario, it could be observed how users adapted ir behavior vualization In th scenario, it could be observed how users adapted ir behavior vualization. People guided by A* used general two strategies fd next target. Eir. People guided by A* used general two strategies fd next target. y looked at ir feet turned fd, or y ignored vualization Eir y looked at ir feet turned fd, or y ignored vualization immediately rotated around ir own axes until target was FOV. Most people with immediately rotated around ir own axes until target was FOV. Most people with immediately knew which direction turn did not look down at ir feet, but rar immediately knew which direction turn did not look down at ir feet, but rar at dtance. at dtance. first second scenario show that participants did not notice a big difference between A* algorithm our algorithm when beg guided through environment. calculated able guide user several targets without showg a negative effect on results. second scenario, however, shows a significant difference between scores two s. Th scenario focuses on uncertaty participants at startg when direction wards target not clear yet. refore, FOV can especially be helpful as long as direction wards a target not clear.

17 Computers 2018, 7, first second scenario show that participants did not notice a big difference between A* algorithm our algorithm when beg guided through environment. calculated able guide user several targets without showg a negative effect on results. second scenario, however, shows a significant difference between scores two s. Th scenario focuses on uncertaty participants at startg when direction wards target not clear yet. refore, FOV can especially be helpful as long as direction wards a target not clear. usability implementation was tested with stardized system usability scale. Figure 12c illustrates a comparon all SUS categories between two implementations. Computers results 2018, dicate 7, 5 that implemented system for both s easy quick learn participants felt confident usg guidance system. It It terestg that participants gave a higher constency level shortest (A*) implementation, although changes less ten comparon implemented. Accordg SUS stard, a score value 68 considered above average usability [28]. With user study, an average SUS score (with σ = 10.06) for (stard deviation σ = 12.58) for shortest was achieved (see Figure 14). Figure Comparon between between duration duration second task second with task straight with straight contrast contrast.. 6. Conclusions 6. Conclusions We presented a complete system DARGS for guidg people door environments with AR. system We presented provides a a complete dynamic system calculation, DARGS for which guidg dependent people on door viewg environments direction with AR. user, system reby provides guaranteeg a dynamic that calculation, always which with dependent FOV on used viewg device. direction DARGS designed user, reby be tegrated guaranteeg as a modular that system always any with application FOV not used dependent device. on DARGS type designed trackg system. be tegrated It also as possible a modular system extend any application with various not guidg dependent methods. on In type th work, trackg we implemented system. It a also particle possible system extend as vualization, but with can various also be guidg replaced methods. with a guidg In th avatar, work, we any implemented kd animated a particle object, system or just aswith vualization, arrows along but it can. also be replaced with a guidg avatar, any first kd evaluation animated presented object, or th just work withshowed arrows along that it was. possible guide people through an door first environment evaluationwith presented th work without showed any furr that it was structions. possible It also guide showed peoplethat through it hard an door measure environment actual with performance a without real scenario. any furr Measurg structions. time not It also sufficient showed enough that it evaluate hard measure system, sce actualpeople performance lookg aa real wrong scenario. direction Measurg always time have not turn sufficient around. enough only difference evaluate system, if y sce are people just searchg lookg for a wrong formation direction or always if y have are provided turn around. with turng only formation. difference if When y talkg are just searchg participants, for formation especially or after if y second are provided task, it with was turng clear that formation. y felt much Whenmore talkg confident participants, especially results also after showed second a better task, performance it was clear that time y for completg felt much more th task. confident In addition results that, y alsowere showed able a better better performance anticipate time direction for completg turn th order task. In addition fd correct way. Th gives a much better feelg when beg guided. As a next step, it would be terestg use different devices, such as a smart phone, order compare applicability application areas where no AR glasses are available. In addition that, we also noticed that, durg study, vualization fluences behavior users. Dependg on complexity, a vualization contag only arrows, floatg objects, or even a human avatar could fluence learng curves users a different way.

18 Computers 2018, 7, that, y were able better anticipate direction turn order fd correct way. Th gives a much better feelg when beg guided. As a next step, it would be terestg use different devices, such as a smart phone, order compare applicability application areas where no AR glasses are available. In addition that, we also noticed that, durg study, vualization fluences behavior users. Dependg on complexity, a vualization contag only arrows, floatg objects, or even a human avatar could fluence learng curves users a different way. Acknowledgments: Th study was supported by research funds from Vienna Science Technology Fund (WWTF ICT15-015). In addition that, we would like thank all participants user studies for ir time. Author Contributions: Georg Gerstweiler Karl Platzer performed manuscript s preparation, implemented methodology ma algorithm, conducted user studies. Hannes Kaufmann superved work. All authors dcussed basic structure manuscript approved fal version. Conflicts Interest: authors declare no conflict terest. References 1. Alnabhan, A.; Tomaszewski, B. INSAR. In Proceedgs Sixth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness (ISA 14), Fort Worth, TX, USA, 4 7 November 2014; ACM: New York, NY, USA, 2014; pp Gerstweiler, G.; Vonach, E.; Kaufmann, H. HyMoTrack: A mobile AR navigation system for complex door environments. Sensors 2015, 16. [CrossRef] [PubMed] 3. Snook, G. Simplified 3D Movement Pathfdg Usg Navigation Meshes. In Game Programmg Gems; DeLoura, M., Ed.; Charles River Media: Newn Centre, MA, USA, 2000; pp Hart, P.E.; Nilsson, N.J.; Raphael, B. Correction A Formal Bas for Heurtic Determation Mimum Cost Paths. ACM SIGART Bull. 1972, [CrossRef] 5. Dijkstra, E.W. A note on two problems connexion with graphs. Numer. Math. 1959, 1, [CrossRef] 6. Al Delail, B.; Weruaga, L.; Zemerly, M.J. CAViAR: Context aware vual door augmented reality for a University Campus. In Proceedgs 2012 IEEE/WIC/ACM International Conference on Web Intelligence Intelligent Agent Technology Workshops (WI-IAT 2012), Macau, Cha, 4 7 December 2012; pp Kasprzak, S.; Komnos, A.; Barrie, P. Feature-based door navigation usg augmented reality. In Proceedgs 9th International Conference on Intelligent Environments, Ans, Greece, July 2013; pp [CrossRef] 8. Huey, L.C.; Sebastian, P.; Drieberg, M. Augmented reality based door positiong navigation ol. In Proceedgs 2011 IEEE Conference on Open Systems (ICOS), Langkawi, Malaysia, 5 28 September 2011; pp [CrossRef] 9. Reitmayr, G.; Schmalstieg, D. Location based applications for mobile augmented reality. In Proceedgs Fourth Australasian User Interface Conference on User Interfaces 2003; Australian Computer Society, Inc.: Darlghurst, Australia, 2003; pp Mulloni, A.; Seichter, H.; Schmalstieg, D. Hheld augmented reality door navigation with activity-based structions. In Proceedgs 13th International Conference on Human Computer Interaction with Mobile Devices Services (MobileHCI 11), Sckholm, Sweden, 30 August 2 September 2011; p Kim, J.; Jun, H. Vion-based location positiong usg augmented reality for door navigation. IEEE Trans. Consum. Electron. 2008, 54, [CrossRef] 12. Möller, A.; Kranz, M.; Diewald, S.; Roalter, L.; Huitl, R.; Sckger, T.; Koelle, M.; Ldemann, P.A. Experimental evaluation user terfaces for vual door navigation. In Proceedgs 32nd Annual ACM Conference on Human Facrs Computg Systems (CHI 14), Toron, ON, Canada, 26 April 1 May 2014; ACM Press: New York, NY, USA, 2014; pp Möller, A.; Kranz, M.; Huitl, R.; Diewald, S.; Roalter, L. A Mobile Indoor Navigation System Interface Adapted Vion-based Localization. In Proceedgs 11th International Conference on Mobile Ubiquius Multimedia (MUM 12), Ulm, Germany, 4 6 December 2012; ACM: New York, NY, USA, 2012; pp. 4:1 4:10.

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