Visually impaired people face many daily encumbrances. Traditional visual enhancements do not suffice to navigate indoor environments. In this paper, we explore path finding algorithms such as Dijkstra and A* combined with graph coloring to find a safest and shortest path for visual impaired people to navigate indoors. Our mobile application is based on a database which stores the locations of several spots in the building and their corresponding label. Visual impaired people select the start and destination when they want to find their way, and our mobile application will show the appropriate path which guarantees their safety

Bulusu, Nirupama

Du, Pei

English

PDXScholar (Portland State University)

Portland State University PDXScholar Electrical and Computer Engineering Faculty Publications and Presentations Electrical and Computer Engineering 6-2022 Poster: Indoor Navigation for Visually Impaired People with Vertex Colored Graphs Pei Du Portland State University, pdu@pdx.edu Nirupama Bulusu Portland State University, nbulusu@pdx.edu Follow this and additional works at: https://pdxscholar.library.pdx.edu/ece_fac  Part of the Computer Engineering Commons, and the Electrical and Computer Engineering Commons Let us know how access to this document benefits you. Citation Details Du, P. and Bulusu, N. (2022). Poster: Indoor Navigation for Visually Impaired People with Vertex Colored Graphs. In The 20th Annual International Conference on Mobile Systems, Applications and Services (MobiSys ’22), June 25–July 1, 2022, Portland, OR, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3498361.353879 This Article is brought to you for free and open access. It has been accepted for inclusion in Electrical and Computer Engineering Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: pdxscholar@pdx.edu. Poster: Indoor Navigation for Visually Impaired People withVertex Colored GraphsPei Du∗Portland State UniversityPortland, Oregon, USApdu@pdx.eduNirupama BulusuPortland State UniversityPortland, Oregon, USAnbulusu@pdx.eduABSTRACTVisually impaired people face many daily encumbrances. Tradi-tional visual enhancements do not suffice to navigate indoor envi-ronments. In this paper, we explore path finding algorithms suchas Dijkstra and A* combined with graph coloring to find a safestand shortest path for visual impaired people to navigate indoors.Our mobile application is based on a database which stores thelocations of several spots in the building and their correspondinglabel. Visual impaired people select the start and destination whenthey want to find their way, and our mobile application will showthe appropriate path which guarantees their safety.CCS CONCEPTS• Human-centered computing → Accessibility; Ubiquitous andmobile computing; Interaction design.KEYWORDSgraph coloring, shortest path finding algorithmACM Reference Format:Pei Du and Nirupama Bulusu. 2022. Poster: Indoor Navigation for VisuallyImpaired People with Vertex Colored Graphs. In The 20th Annual Interna-tional Conference on Mobile Systems, Applications and Services (MobiSys ’22),June 25–July 1, 2022, Portland, OR, USA. ACM, New York, NY, USA, 2 pages.https://doi.org/10.1145/3498361.35387971 INTRODUCTIONVisual impairment is a pervasive disability which consists of limitedvision, blurry vision, light sensitivity and blind spots[1]. Accordingto CDC, nearly 21 million people in United States have low visionproblems, encountering daily encumbrances in activities such as in-door wayfinding, relying traditionally on audio and visual guidance[2–9]. However, these techniques do not always help them find thesafest and shortest way to their destination. We build our wayfind-ing application based on graph theory [10] and our previous work[11]. A volunteer can use our system to automatically collect datafor Assisted Navigation Systems, replacing human effort with deeplearning, augmented reality and indoor positioning technologies.Our application depends on the database that the volunteer col-lects. We consider the locations such as doors, elevators, stairs asPermission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.For all other uses, contact the owner/author(s).MobiSys ’22, June 25–July 1, 2022, Portland, OR, USA© 2022 Copyright held by the owner/author(s).ACM ISBN 978-1-4503-9185-6/22/06.https://doi.org/10.1145/3498361.3538797(a)(b)Figure 1: Graph Coloring (a) Basement (b) First floorgraph nodes. Based on the labels in the database, we classify themas safe, cautious, and unsafe and mark them with different colors.And then we apply shortest path algorithms colored graphs to findthe safest and shortest way for vision impaired people.The contributions of our paper is to design an algorithm whichuses Dijkstra and A* algorithms on colored graph to find the safestand shortest way between several floors. We next describe our workin graph coloring (Section 2).2 GRAPH COLORINGGraph Coloring is a step which needs to be accomplished beforewe run indoor wayfinding application. We use the floor plan of aspecific building and consider it as a graph and classify the nodes asthree categories, safe nodes, nodes with caution, and unsafe nodes.587MobiSys ’22, June 25–July 1, 2022, Portland, OR, USA Du and BulusuThe colors assigned to each category are green, orange and redrespectively. For visually impaired people, it is very dangerous forthem to use stairs to reach a different floor. Hence the stairs shouldbe always be marked as red. In contrast, the elevators are veryfriendly to them, so we mark it as green. For some obstacles, suchas desks which may have potential hazard or interrupt the user,we mark them as orange. If the user cannot find a path with greennodes, the application will show a path with a mixture of greenand orange nodes, and the users should proceed with caution. InFigure 1, we assign a color to different spots and connect them withweighted edge based on the distance calculated from the positions.We create two graphs𝐺1,𝐺2 to represent two floors and removered nodes first to get𝐺1𝑟 ,𝐺2𝑟 . Then, we also need to remove orangenodes𝐺1𝑜 ,𝐺2𝑜 . In order to guarantee the safety of the user,𝐺1𝑜 ,𝐺2𝑜have higher priority than𝐺1𝑟 ,𝐺2𝑟 . Only when the algorithm cannotfind a path in𝐺1𝑜 ,𝐺2𝑜 , we need to choose a path with orange nodesand use 𝐺1𝑟 ,𝐺2𝑟 . For the floor the user starts their path, we findthe shortest path from the start point to each elevator, save thepath and total weights. Similarly, for the floor where the user’sdestination lies, we find the shortest path from each elevator to thedestination. We concatenate a final route with same connectingpoint (i.e. the elevator) and compare the total weights of each routeand return the shortest route. Algorithm 1 shows the pseudo codeof safest path finding algorithm. We test our algorithm on AndroidStudio, search our way from node 0 to node 9 and we attach thescreenshot (see Figure 2) to show the safest and shortest path wefind for the graph in Figure 1.Algorithm 1 Safest Path Finding Algorithm Between Two FloorsRequire: 𝑉𝑠𝑡𝑎𝑟𝑡 ∈ 𝐺1 (𝐸1,𝑉1),𝑉𝑒𝑛𝑑 ∈ 𝐺2 (𝐸2,𝑉2)Ensure: (𝑉𝑠𝑡𝑎𝑟𝑡 ...𝑉𝑒𝑙𝑒𝑣𝑎𝑡𝑜𝑟 ...𝑉𝑒𝑛𝑑 )1: Remove Red Nodes in 𝐺1,𝐺2, get 𝐺1𝑟 ,𝐺2𝑟2: Remove Orange Nodes in 𝐺1𝑟 ,𝐺2𝑟 , get 𝐺1𝑜 ,𝐺2𝑜3: for 𝑉𝑖 ∈ 𝑉𝑒𝑙𝑒𝑣𝑎𝑡𝑜𝑟 𝑖𝑛 𝐺1𝑜 do4: Run Dijkstra or A* on 𝐺1𝑜 from 𝑉𝑠𝑡𝑎𝑟𝑡 to 𝑉𝑖5: if Shortest path found then6: save path7: else if No path found then8: Run Dijkstra or A* on 𝐺1𝑟 from 𝑉𝑠𝑡𝑎𝑟𝑡 to 𝑉𝑖9: save path10: end if11: end for12: for 𝑉𝑖 ∈ 𝑉𝑒𝑙𝑒𝑣𝑎𝑡𝑜𝑟 𝑖𝑛 𝐺2𝑜 do13: Run Dijkstra or A* on 𝐺2𝑜 from 𝑉𝑖 to 𝑉𝑒𝑛𝑑14: if Shortest path found then15: save path16: else if No path found then17: Run Dijkstra or A* on 𝐺2𝑟 from 𝑉𝑖 to 𝑉𝑒𝑛𝑑18: end if19: end for20: Concatenate path based on same 𝑉𝑒𝑙𝑒𝑣𝑎𝑡𝑜𝑟 and compare all thepathsreturn shortest path (𝑉𝑠𝑡𝑎𝑟𝑡 ...𝑉𝑒𝑙𝑒𝑣𝑎𝑡𝑜𝑟 ...𝑉𝑒𝑛𝑑 )Figure 2: Test Result3 CONCLUSIONWe have proposed an algorithm for visually impaired people to helpthem find their way safely and quickly in the indoor environment.Our method doesn’t need any installations for sensors or visiblemarks, only one volunteer can collect positions and labels of nodesby using automated AR-based annotation tool and then color thegraph built from the floor plan. The users can hold a mobile deviceto guide their way to destination. We just finished the design ofthe algorithm and test it in the emulator. In the future, we have tocalibrate the position and use AR tools or audio guidance to helpthem find their way in the real environment.REFERENCES[1] AOA (American Optometric Association). Common types of low vision. 2015.[2] Yuhang Zhao, Sarit Szpiro, and Shiri Azenkot. Forsee: A customizable head-mounted vision enhancement system for people with low vision. pages 239–249.International ACM SIGACESS Conference on Computers and Accessibility, 2015.[3] Yuhang Zhao, Sarit Szpiro, Lei Shi, and Shiri Azenkot. Designing and evaluatinga customizable head-mounted vision enhancement system for people with lowvision. ACM Transactions on Accessible Computing, Dec 2019.[4] Yuhang Zhao, Sarit Szpiro, Jonathan Knighten, and Shiri Azenkot. Cuesee:Exploring visual cues for people with low vision to facilitate a visual search task.pages 73–84. ACM UbiComp, Sep 2016.[5] Yuhang Zhao, Michele Hu, Shafeka Hashash, and Shiri Azenkot. Understandinglow vision people’s visual perception on commercial augmented reality glasses.Number 4170-4181. ACM CHI, 2017.[6] Yuhang Zhao, Elizabeth Kupferstein, Brenda V. Castro, Steven Feiner, and ShiriAzenkot. Designing ar visualizations to facilitate stair navigation for people withlow vision. UIST 2019, Aug 2019.[7] Yuhang Zhao, Elizabeth Kupferstein, Hathaitorn Rojnirun, Leah Findlater, andShiri Azenkot. The effectiveness of visual and audio wayfinding guidance onsmartglasses for people with low vision. CHI, 2020.[8] Dragan Ahmetovic, Cole Gleason, Kris M. Kitani, Hironobu Takagi, and ChiekoAsakawa. Navcog: Turn-by-turn smartphone navigation assistant for people withvisual impairments or blindness. In Proceedings of the 13th International Web forAll Conference, W4A ’16, New York, NY, USA, 2016. Association for ComputingMachinery.[9] Manaswi Saha, Alexander J. Fiannaca, Melanie Kneisel, Edward Cutrell, andMeredith Ringel Morris. Closing the gap: Designing for the last-few-meterswayfinding problem for people with visual impairments. In The 21st InternationalACM SIGACCESS Conference on Computers and Accessibility, ASSETS ’19, page222–235, New York, NY, USA, 2019. Association for Computing Machinery.[10] Mikio Kano and Xueliang Li. Monochromatic and heterochromatic subgraphs inedge-colored graphs - a survey. Graph. Comb., 24(4):237–263, sep 2008.[11] Pei Du and Nirupama Bulusu. An automated ar-based annotation tool for indoornavigation for visually impaired people. In The 23rd International ACM SIGAC-CESS Conference on Computers and Accessibility, ASSETS ’21, New York, NY, USA,2021. Association for Computing Machinery.588

Poster: Indoor Navigation for Visually Impaired People with Vertex Colored Graphs

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Poster: Indoor Navigation for Visually Impaired People with Vertex Colored Graphs

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