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Landmark-based instructions for pedestrian navigation systems using OSM

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Pedestrian navigation systems available today still use paradigms developed for car navigation. We present a novel landmark-based
pedestrian navigation model using only OpenStreetMap data, which is open and available globally. This approach ensures that our landmark
navigation model is widely applicable, rather than restricted to a certain area with exceptional data sources. Our contributions cover
algorithms for extraction, weighing, and selection of landmarks based on their suitability, as well as the generation of landmark-based
navigation instructions for pedestrian routes. Initial tests with pedestrians show promising results by confirming that our weighted landmark
selection reduces the number of navigation errors and revealing future challenges for the generation of intuitive pedestrian navigation
instructions.

Published in: Technology
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Landmark-based instructions for pedestrian navigation systems using OSM

  1. 1. Anita Graser Scientist, Center for Mobility Systems – AIT Austrian Institute of Technology Plaza Mayor – cc-by Kris Arnold on Flicker LANDMARK-BASED INSTRUCTIONS FOR PEDESTRIAN NAVIGATION SYSTEMS USING OSM
  2. 2. We want to …  Compute realistic pedestrian routes  Compute pedestrian navigation instructions with landmarks  Use only commonly available data, i.e. OSM OUR POINT OF DEPARTURE
  3. 3. 1. Constructing a pedestrian routing graph from OSM 2. Extracting landmarks from OSM 3. Generating landmark-based pedestrian instructions OUTLINE
  4. 4.  using information from globally available OpenStreetMap database  based on sidewalks not on roads  considering way quality criteria (based on selected route attributes)  including road crossing and square crossing TOPIC 1: CONSTRUCTING A PEDESTRIAN ROUTING GRAPH FROM OSM 431.05.2017 Naumann S. and Kovalyov M.Y. (accepted) Pedestrian Route Search based on OpenStreetMap. 3th SCIENTIFIC AND TECHNICAL CONFERENCE TRANSPORT SYSTEMS THEORY AND PRACTICE, Katowice, Poland, September 19-21, 2016. Naumann, S., Czogalla, O., Kühner, F. (2015) A Safety Index for Road Crossing, Future Active Safety Technology Towards zero traffic accidents, FAST- zero 2015 Symposium, Gothenburg, Sweden.
  5. 5. INTEGRATION OF PEDESTRIAN SQUARES INTO THE ROUTING GRAPH 531.05.2017Graser, A. (2016) Integrating Open Spaces Into OpenStreetMap Routing Graphs for Realistic Crossing Behavior in Pedestrian Navigation. GI_Forum ‒ Journal for Geographic Information Science, 1-2016, 217-230, doi:10.1553/giscience2016_01_s217.
  6. 6. experimental implementation in QGIS ROUTING ON THE PEDESTRIAN GRAPH 31.05.2017
  7. 7. TOPIC 2: EXTRACTING POTENTIAL LANDMARKS FROM OSM 731.05.2017 OSM import with ogr2ogr PERRON views
  8. 8. POINT LANDMARKS 31.05.2017 …
  9. 9. POLYGON LANDMARKS 31.05.2017 …
  10. 10. WEIGHTS FOR LANDMARKS 1031.05.2017 Descriptive name Weight …
  11. 11. Pedestrian-centered navigation instructions  using information from globally available OpenStreetMap database  automatic selection of most suitable landmark TOPIC 3: GENERATING LANDMARK-BASED PEDESTRIAN INSTRUCTIONS 1131.05.2017 Turn left after Café Hegelhof
  12. 12. 1. Extracting decision point candidates along the route 2. Filtering & merging of decision point candidates 3. Determining crossing types 4. Splitting the route into episodes between decision points 5. Selecting landmarks for decision points 6. Computing instructions per episode 7. Generating internationalized text for instructions INSTRUCTION CALCULATION PROCESS
  13. 13. Possible reasons are:  Start of the route  Mode of transport change  Direction change  Name change  Functional Road Class (FRC) change  Form of Way (FOW) change  Road crossing (at start of each link that is a crossing)  Begin / end of stairs, bridges, tunnels, roundabouts… STEPS 1 – EXTRACTING DECISION POINTS
  14. 14. STEPS 2 - REMOVING UNNCESSARY DECISION POINTS
  15. 15. STEP 3 – CROSSING TYPES
  16. 16. Splitting  episodes between decision points STEP 4 – SPLITTING AT DECISION POINTS
  17. 17. Approaches  All named buildings in buffer [Elias & Sester 2002]  Most unique building at decision point [Elias 2003]  Rule-based using categories [Duckham et al. 2010, Dräger & Koller 2012]  Features with highest „landmarkness“ [Quesnot & Roche 2014]  Neuronal network [Zhu & Karimi 2015] STEP 5 – SELECTING LANDMARKS
  18. 18. 𝑆 = ( 𝑑 𝑚𝑎𝑥 − 𝑑 ∗ 𝑤 𝑑 − 𝑐 𝑚𝑎𝑥 − 𝑐 ∗ 𝑑 𝑚𝑎𝑥 𝑐 𝑚𝑎𝑥 ∗ 𝑤𝑐 + 𝑠 ∗ 𝑤𝑠 + 𝑙 ∗ 𝑤𝑙) ∗ 𝑣 where  d is the distance between decision point and landmark,  dmax is the maximum distance for a candidate to be considered,  c is the landmark category weight,  cmax is the maximum landmark category weight,  s is the side of the landmark relative to the next turn: same side (1) or other side (0),  l is the location of the landmark relative to the route: before (1) or after (0) the decision point,  v is the visibility of the landmark: visible (1) or hidden (0), and  wd, wc, ws, wl are the weights for the terms for distance, category suitability, side, and location. STEP 5 – SELECTING LANDMARKS Land- marks 10 nearest point & 10 nearest polygon LMs LM candidates sort by score Best LM
  19. 19. STEP 6 GENERATING INSTRUCTIONS
  20. 20. Handling micro mapping … STEP 6 GENERATING INSTRUCTIONS
  21. 21. # 0=landmark, 1=on street RoadInstruction.ROUTE_START = Start {0} {1} Preposition.BEFORE = before the {0} Preposition.AT = at the {0} Preposition.AFTER = after the {0} amenity=library = library RoadInstruction.onto_start_street = on {0} STEP 7 INTERNATIONALIZATION # 0=landmark, 1=on street RoadInstruction.ROUTE_START = Starten Sie {0} {1} Preposition.BEFORE_m = vor dem {0} Preposition.BEFORE_f = vor der {0} Preposition.BEFORE_n = vor dem {0} ... amenity=library = die Bücherei RoadInstruction.onto_start_street_m = auf dem {0} RoadInstruction.onto_start_street_f = auf der {0} RoadInstruction.onto_start_street_n = auf dem {0}
  22. 22. OPEN ROUTE FORMAT 2531.05.2017 https://github.com/dts-ait/sproute-json-route-format
  23. 23. http://bit.do/perron DEMO WEBSITE
  24. 24.  Further improvement of navigation instructions  Field tests in Vienna and Magdeburg  Calibration of weights for landmark selection algorithm ONGOING AND FUTURE WORK
  25. 25. CONTACT Anita Graser – anita.graser@ait.ac.at @underdarkGIS

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