1. Christian Mandel Bernd Krieg-Brückner Bernd Gersdorf Christoph Budelmann Marcus-Sebastian Schröder Navigation Aid for Mobility Assistants Joint CEWIT-TZI-acatech Workshop “ICT meets Medicine and Health” ICTMH 2013

2. Compensate declining physical and cognitive capabilities Provide navigation assistance that considers specific needs: Precise localization Route planning respecting vehicle specific constraints User interface suitable for the elderly Overview: Walker with NavigationAid IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

3. Two versions of OdoWheel Inertial Measurement Unit (IMU) Current revision comprises 3-axis acceleration sensor and gyrometer Bluetooth [Low energy] radio link Battery [solar] driven power supply 32 bit microcontroller Extended Kalman Filter fuses accelerometer- and gyro-data → Odometry Additional Hardware Component: OdoWheel IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

4. OSM description of road network, land usage, buildings, … Open community project Based on user-recorded GPS track logs, or vectorization of aerial images XML vector representation with atomic building blocks: points, ways, relations Free tagging system for annotation of properties Handy modeling tools such as the Java-OpenStreetMap-Editor (JOSM) Environment Representation: OpenStreetMap (OSM) IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

5. Environment Representation: OpenStreetMap (OSM) Road network stored in PMR-Quadtree Space partitioning data structure sorting its entries into buckets Bucket is split into four child buckets when |entries| exceeds threshold c Let N := |position hypotheses| and M:= |road segments| ↓ O(c*N) instead of O(M*N) distance(road segment, position) queries for finding closest road segment to given pose hypothesis when using PMR-Quadtree [1] E.G. Hoel and H. Samet: Efficient Processing of Spatial Queries in Line Segment Databases. In: Advances in Spatial Databases; Vol.: 525 of Lecture Notes in Computer Science, pages 237-256. Springer Verlag, 1991. IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

6. Monte Carlo Localization: Motivation [2] GPS – Essentials of Satellite Navigation Compendium. uBlox, 2009. Online: http://www.u-blox.ch/images/downloads/Product_Docs/GPS_Compendium%28GPS-X-02007%29.pdf Sources of GPS errors Multipath signals reflected from buildings, trees, mountains, … IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

7. Monte Carlo Localization: Overview Motion Update Sensor Update Resampling Model estimate of current position by set of samples Move each pose hypothesis according to: Odometry measurements Translational, and rotational noise IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

8. Monte Carlo Localization: Overview Motion Update Sensor Update Resampling Score each pose hypothesis according to: Distance to GPS measurement Distance to closest OSM path Type of closest OSM path, kind of entity passed over during last motion update IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

9. Monte Carlo Localization: Overview Motion Update Sensor Update Resampling Rebuild set of samples for next frame Sample’s score determines probability to occur in the new set IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

10. Estimated state is a pose in 2-D Particle implementation: Motion model: State transition based on traveled distance and rotation Update of sample position Monte Carlo Localization: Motion Update IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

11. Monte Carlo Localization: Sensor Update Sensor model: position measurement from a connected GPS device virtual path distance measurement (always zero) virtual measurement describing expected behavior Computation of weighting: IntroductionOutdoor LocalizationPath PlanningUser InterfaceResults / Outlook

12. IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook OSM Based Route Planning Uses 22 different path types including oneway paths Platform/user-sepcific weighting Uses A-star algorithm Computation of turn advices

13. Map View of User Interface detailed representation of surroundings immediate walking direction abstract path network with walking directionplanned path current position IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

14. Compass View of User Interface abstract path network with walking direction immediate walking direction IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

15. Selecting (special) Targets in User Interface push to speak target location type in target location push to select special target IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

16. Localization Example Estimated trajectory (red) vs. GPS trajectory (green) IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

17. Future Work Outdoor Localizer Route Planning Evaluation Hardware Integration Vehicle Platforms Barthel Index NASA Task Load Index IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook

18. Navigation Aid for Mobility Assistants Joint CEWIT-TZI-acatech Workshop “ICT meets Medicine and Health” ICTMH 2013 Thank you for your attention! Questions?