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Simultaneous Localization and Mapping�for Pedestrians �using only Foot-Mounted Inertial Sensors

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Slides from UbiComp 2009 on FootSLAM: SLAM using human pedestrian odometry using foor mounted inertial sensors. Applications: pedestrian indoor navigation, automated mapping. see

Text of Simultaneous Localization and Mapping�for Pedestrians �using only Foot-Mounted Inertial Sensors

  • 1. Simultaneous Localization and Mapping for Pedestriansusing only Foot-Mounted Inertial Sensors Patrick Robertson, Michael Angermann, Bernhard Krach German Aerospace Center (DLR) B. Krach is now with EADS Germany

2. Raw NavShoe Odometry ResultsAlgorithm: Extended Kalman Filter with Zero Velcocity Updates (Foxlin) NavShoe INS produced reasonable results NavShoe INS had larger heading slips; stand alone, but still unbounded error growth unbounded error begins to rise earlier 3. State of the Art: Use MapsInertial sensors used indoors achieve accurate positioningwhen used in conjunction with mapsKrach, Robertson: WPNC 08, PLANS 08+Widyawan, Klepal, Beauregard: WPNC 08 Woodman, Harle: UbiComp 2008But what if the map is unknown? 4. So, could we derive a map from this? Nave approach:Transfer the raw odometry trace to a piece of wire and bend it bit by bit so that similar areas overlap 5. SLAM in Robotics Simultaneous Localization and Mapping - identified by robotics community in mid 80s! Premise:Localization using odometry and sensing of knownlandmarks is easy!Mapping of landmarks given known location and orientation (pose) is easy! Simultaneous Localization and Mapping is hard! 6. What about SLAM for Humans? Human pedestrians are not robots but share some similarities with them Visual sensors (eyes) 'Odometry' (in humans: sensed by proprioception) Path and planning and execution In humans, we usually have little or no direct 'access' to most of these senses and functions Our central assumption: The pedestrian is able to actively control motion without violating physical constraints (i.e. walls, etc) 7. A Person Processes Numerous Visual Inputs 8. Bayesian Formulation: DBNTime k-1Time k Time k+1 Pose PPPU: Actual step taken(pose change vector) Measured U UU Step Error states ofZuthe odometryZu E Zu EZuEIntIntInt Intention what the person wants to do VisVisVisVisual informationwhat the person sees Environment = Map constant over time Map 9. Intuitive Explanation of the Sequential Monte Carlo AlgorithmFootSLAM lets particles, or hypotheses, explore the statespace of odometry errors, like evolution of driftIn this way, every particle is trying a slightly differently bentpiece of wireParticles are weighted by their compatibility with their individual map optional sensor readings, such as GPS, magnetometer We can show that this is optimal in the Bayesian sense! 10. Experiments and ResultsMeasurement data taken from a pedestrian wearing a foot mounted IMUTwo scenarios:Indoor onlyOutdoor indoor - outdoor sequence 11. Video See 12. Resulting Maps 13. Resulting Maps 14. Relative Position Accuracy - Indoors No Scale Adaptation was Performed 15. Outdoor-Indoor-Outdoor 16. Concluding NotesFootSLAM effectively bounds the otherwise unbounded error growthwithout the need for pre-existing maps! FootSLAM (like all forms of SLAM) is inherently invariant to rotation,translation and scale In mixed scenarios, the resulting maps are globally and precisely anchoredusing GPS Our future work:Map building with multiple users;crowdsourcing collaborative mapping Movies: 17. Thank you!Movies: