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Compensate declining physical and cognitive capabilities

Overview: Walker with NavigationAid. 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.

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Compensate declining physical and cognitive capabilities

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  1. Overview: Walker with NavigationAid • 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

  2. Additional Hardware Component: OdoWheel • 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

  3. Environment Representation: OpenStreetMap (OSM) • 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 toolssuch as the Java-OpenStreetMap-Editor (JOSM)

  4. 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 ofSpatialQueries in Line Segment Databases. In: Advances in Spatial Databases; Vol.: 525 ofLecture Notes in Computer Science, pages 237-256. Springer Verlag, 1991.

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

  6. Monte Carlo Localization: Overview • Model estimate of current position by set of samples • Move each pose hypothesis according to: • Odometry measurements • Translational, and rotational noise Motion Update Sensor Update Resampling

  7. Monte Carlo Localization: Overview • 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 Motion Update Sensor Update Resampling

  8. Monte Carlo Localization: Overview • Rebuild set of samples for next frame • Sample’s score determines probability to occur in the new set Motion Update Sensor Update Resampling

  9. Monte Carlo Localization: Motion Update • Estimated stateis a pose in 2-D • Particle implementation: • Motion model: • State transition based on traveled distance and rotation • Update of sample position

  10. 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:

  11. OSM Based Route Planning • Uses 22 different path typesincluding oneway paths • Platform/user-sepcific weighting • Uses A-star algorithm • Computation of turn advices

  12. Map View of User Interface abstract path network with walking direction planned path currentposition detailed representation of surroundings immediate walking direction

  13. Compass View of User Interface abstract path network with walking direction immediate walking direction

  14. Selecting (special) Targets in User Interface type in target location push to speak target location push to select special target

  15. Localization Example • Estimated trajectory (red) vs. GPS trajectory (green)

  16. Future Work • Outdoor Localizer • Route Planning • Evaluation • Hardware Integration • Vehicle Platforms • Barthel Index • NASA Task Load Index

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

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