Simultaneous Localization and Mappingfor Pedestrians using only Foot-Mounted Inertial Sensors

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

Raw NavShoe Odometry Results Algorithm: 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

State of the Art: Use Maps Inertial sensors used indoors achieve accurate positioning when used in conjunction with maps Krach, Robertson: WPNC 08, PLANS 08+ Widyawan, Klepal, Beauregard: WPNC 08 Woodman, Harle: UbiComp 2008 But what if the map is unknown?

So, could we derive a map from this? Naïve approach: “Transfer the raw odometry trace to a piece of wire and bend it bit by bit so that similar areas overlap”

SLAM in Robotics Simultaneous Localization and Mapping - identified by robotics community in mid ‘80s! Premise: Localization using odometry and sensing of known landmarks is easy! Mapping of landmarks given known location and orientation (pose) is easy! Simultaneous Localization and Mapping is hard!

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)

A Person Processes Numerous Visual Inputs

Bayesian Formulation: DBN Time k-1 Time k Time k+1 Pose P P P U: Actual step taken (pose change vector) Measured U U U Step Error states of Zu the odometry Zu E Zu E Zu E Int Int Int Intention „what the person wants to do“ Vis Vis Vis Visual information „what the person sees“ “Environment” = Map … constant over time Map

Intuitive Explanation of the Sequential Monte Carlo Algorithm FootSLAM lets particles, or hypotheses, explore the state space of odometry errors, like evolution of drift In this way, every particle is trying a slightly “differently bent piece of wire” Particles 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!

Experiments and Results Measurement data taken from a pedestrian wearing a foot mounted IMU Two scenarios: Indoor only Outdoor – indoor - outdoor sequence

Video See http://www.kn-s.dlr.de/indoornav

Resulting Maps

Relative Position Accuracy - Indoors No Scale Adaptation was Performed

Outdoor-Indoor-Outdoor

Concluding Notes FootSLAM effectively bounds the otherwise unbounded error growth without 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 anchored using GPS Our future work: Map building with multiple users; “crowdsourcing” collaborative mapping Movies: http://www.kn-s.dlr.de/indoornav/

Thank you! Movies: http://www.kn-s.dlr.de/indoornav/

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Simultaneous Localization and Mappingfor Pedestrians using only Foot-Mounted Inertial Sensors

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