PowerPoint-Prsentation
Christian MandelBernd Krieg-BrcknerBernd GersdorfChristoph BudelmannMarcus-Sebastian SchrderNavigation Aid for Mobility AssistantsJoint CEWIT-TZI-acatech WorkshopICT meets Medicine and HealthICTMH 2013
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 NavigationAidIntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / OutlookTwo 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 / OutlookOSM 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)
Environment Representation: OpenStreetMap (OSM)
IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / OutlookEnvironment 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 / OutlookMonte 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 / OutlookMonte Carlo Localization: OverviewMotion UpdateSensor UpdateResamplingModel estimate of current position by set of samples
Move each pose hypothesis according to:
Odometry measurements
Translational, and rotational noise
IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / OutlookMonte Carlo Localization: OverviewMotion UpdateSensor UpdateResamplingScore 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 / OutlookMonte Carlo Localization: OverviewMotion UpdateSensor UpdateResamplingRebuild set of samples for next frame
Samples score determines probability to occur in the new set
IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / OutlookEstimated 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 / OutlookMonte Carlo Localization: Sensor UpdateSensor 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 / OutlookIntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / OutlookOSM Based Route PlanningUses 22 different path typesincluding oneway paths
Platform/user-sepcific weighting
Uses A-star algorithm
Computation of turn advices
Map View of User Interfacedetailed representation of surroundingsimmediate walking directionabstract path network with walking directionplanned pathcurrentpositionIntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook
Compass View of User Interfaceabstract path network with walking directionimmediate walking directionIntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook
Selecting (special) Targets in User Interfacepush to speak target locationtype in target locationpush to select special targetIntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook
Localization ExampleEstimated trajectory (red) vs. GPS trajectory (green)IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / OutlookFuture WorkOutdoor Localizer
Route Planning
Evaluation
Hardware Integration
Vehicle Platforms
Barthel IndexNASA Task Load Index
IntroductionOutdoor LocalizationRoute PlanningUser InterfaceResults / Outlook
Navigation Aid for Mobility AssistantsJoint CEWIT-TZI-acatech WorkshopICT meets Medicine and HealthICTMH 2013Thank you for your attention! Questions?