Christian Mandel Bernd Krieg-Brückner Bernd Gersdorf Christoph Budelmann Marcus-Sebastian Schröder...
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- Slide 1
- Christian Mandel Bernd Krieg-Brckner Bernd Gersdorf Christoph
Budelmann Marcus-Sebastian Schrder Navigation Aid for Mobility
Assistants Joint CEWIT-TZI-acatech Workshop ICT meets Medicine and
Health ICTMH 2013
- Slide 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
- Slide 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
- Slide 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
- Slide 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
- Slide 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
- Slide 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
- Slide 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
- Slide 9
- Monte Carlo Localization: Overview Motion Update Sensor Update
Resampling Rebuild set of samples for next frame Samples score
determines probability to occur in the new set IntroductionOutdoor
LocalizationRoute PlanningUser InterfaceResults / Outlook
- Slide 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
- Slide 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
- Slide 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
- Slide 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
- Slide 14
- Compass View of User Interface abstract path network with
walking direction immediate walking direction IntroductionOutdoor
LocalizationRoute PlanningUser InterfaceResults / Outlook
- Slide 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
- Slide 16
- Localization Example Estimated trajectory (red) vs. GPS
trajectory (green) IntroductionOutdoor LocalizationRoute
PlanningUser InterfaceResults / Outlook
- Slide 17
- Future Work Outdoor Localizer Route Planning Evaluation
Hardware Integration Vehicle Platforms Barthel Index NASA Task Load
Index IntroductionOutdoor LocalizationRoute PlanningUser
InterfaceResults / Outlook
- Slide 18
- Navigation Aid for Mobility Assistants Joint CEWIT-TZI-acatech
Workshop ICT meets Medicine and Health ICTMH 2013 Thank you for
your attention! Questions?