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Page 1: Simultaneous Localization and Mappingfor Pedestrians using only Foot-Mounted Inertial Sensors

Simultaneous Localization and Mappingfor Pedestrians

using only Foot-Mounted Inertial Sensors

Patrick Robertson, Michael Angermann, Bernhard KrachGerman Aerospace Center (DLR)B. Krach is now with EADS Germany

Page 2: Simultaneous Localization and Mappingfor Pedestrians using only Foot-Mounted Inertial Sensors

Raw NavShoe Odometry Results

NavShoe INS produced reasonable resultsstand alone, but still unbounded error growth

NavShoe INS had larger heading slips;unbounded error begins to rise earlier

Algorithm: Extended Kalman Filter with Zero Velcocity Updates (Foxlin)

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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 08Woodman, Harle: UbiComp 2008

But what if the mapis unknown?

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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”

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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!

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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 functionsOur central assumption:

The pedestrian is able to actively control motion without violating physical constraints (i.e. walls, etc)

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A Person Processes Numerous Visual Inputs

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P

U

Zu E

Int

Vis

Map

Visual information„what the person sees“

Intention„what the person wants to do“

Error states of the odometry

U: Actual step taken (pose change vector)

PoseP

U

Zu E

Int

Vis

P

U

Zu E

Int

Vis

Time k-1 Time k Time k+1

MeasuredStep Zu

“Environment” = Map … constant over time

Bayesian Formulation: DBN

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Intuitive Explanation of the Sequential Monte Carlo Algorithm

FootSLAM lets particles, or hypotheses, explore the state space of odometry errors, like evolution of driftIn this way, every particle is trying a slightly “differently bent piece of wire”Particles are weighted by their “compatibility” with

their individual mapoptional sensor readings, such as GPS, magnetometer

We can show that this is optimal in the Bayesian sense!

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Experiments and Results

Measurement data taken from a pedestrian wearing a foot mounted IMU

Two scenarios:Indoor onlyOutdoor – indoor - outdoor sequence

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Video

See

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

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Resulting Maps

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Resulting Maps

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Relative Position Accuracy - Indoors No Scale Adaptation was Performed

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Outdoor-Indoor-Outdoor

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Concluding NotesFootSLAM 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/

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Thank you!

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


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