Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling

Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling Patrick Robertson, Michael Angermann, Mohammed Khider, German Aerospace Center (DLR) Slides from: “ Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling”, in Proc. IEEE/ION PLANS 2010, May 2010, Palm Springs, CA, USA.

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), can be measured using inertial sensors
Path and planning and execution
For humans: little or no direct 'access' to senses and functions
Our central assumption:
The pedestrian is able to actively control motion without violating physical constraints (i.e. walls, etc)

Raw NavShoe Odometry Results NavShoe INS produced reasonable results stand 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)

A Person Processes Numerous Visual Inputs

Six ways out of the hexagon
First order Markov process
Location dependent
Time Invariant
Probabilistic map
FootSLAM: Hexagonal Grid over Space Human motion is modelled by a person choosing which edge of the hexagon to cross.

FootSLAM Human Odometry Data Processed with a Particle Filter 5 meters

Human-Recognizable Places

An Example of Placestamps A C G E Physical space F B D 1 2 3 4 5 6 7 8 9 10 11 A B C A F E B D G E D B Timestamped placestamps Perfect association Partial association Unknown association - Arrows denote pedestian‘s trajectory; - letter-coded circles with denote unique places; - colors denote some recognizable aspect of the place

The PlaceSLAM Dynamic Bayesian Network (DBN) P U Z u E Int Vis L, M “ Vis ual impression - what the person sees“ Int ention “where the person wants to go” Time k-1 Measured Step “ Environment” = Human recognizable Places L and FootSLAM Map M ; both are constant over time Odometry Error states Actual step taken (pose change vector) Pose (= location, orientation) Time k P U Z u E Int Vis Z L A Placestamp Place identifier seen Z L A

Intuitive Explanation of the Sequential Monte Carlo Estimator
FootSLAM lets particles, or hypotheses, explore the state space of odometry errors, like evolution of drift as well as the association of places
In this way, every particle is trying a slightly “differently bent piece of wire”
Particles are weighted by their “compatibility” with
their individual PlaceSLAM map
their individual FootSLAM map
optional sensor readings, such as GPS, magnetometer
We can show that this is optimal in the Bayesian sense!

Illustration of Proposal Function 1 d min Particle position d min If particle is closer than d min to some existing place(s) then choose the closest place

Illustration of Proposal Function 2 d min If particle is further than d min from all existing places then choose a new place at the particle‘s current position Particle position New place proposed to be here

Algorithm Summary Perform FootSLAM Weighting and FootSLAM map update Locate closest existing place to particle’s current Pose P Placestamp was reported Select this Identifier (closest place) Choose new identifier None within d min Closest is within d min Multiply weight by P L Multiply weight by Gaussian Likelihood (PLANS paper (12)) Initialise new place’s location to current particle pose P Update place’s location with current particle pose P No placestamp reported Perform for all N p Particles:

Weight update If particle i revisited a place: If particle marked a new place: r cancels out and p L accounts for places being sparse

Intuitive Illustration Place

Intuitive Illustration: Perfect Assoc. Place

Intuitive Illustration: Unknown Assoc. Place d min

Experiments and Results
Measurement data taken from a pedestrian wearing a foot mounted IMU
Placestamps collected during the walk
Two scenarios:
Indoor only
Outdoor – indoor - outdoor sequence
Indoor only: only foot mounted IMU
Mixed scenario: foot mounted IMU as well as GPS and compass sensors

Resulting Maps Large conference Table Canteen

Improvement of Positioning Accuracy

Concluding Notes
PlaceSLAM is a useful adjunct to FootSLAM and improves accuracy and stability
Two main forms of PlaceSLAM: Perfect Association (“press a certain button”) and unknown association (“press any button”)
Error assumptions: Humans are lazy in reporting but do not erroneously report places
Bayesian derivation
Suggested future work:
More experimental data in different sites and for different building sizes and geometries
Map building with multiple users; “crowdsourcing” collaborative mapping
Extend error models, overlapping and multiple places, RFID tags

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

Intuitive Illustration Place

Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling

by patrickrobertson on Oct 26, 2010

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Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling — Presentation Transcript

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