Diese Präsentation wurde erfolgreich gemeldet.
Die SlideShare-Präsentation wird heruntergeladen. ×

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

Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Nächste SlideShare
Ion feet slam 2011 v4
Ion feet slam 2011 v4
Wird geladen in …3
×

Hier ansehen

1 von 25 Anzeige

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

Herunterladen, um offline zu lesen

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

Pesented at IEEE/ION PLANS 2010, May 2010, Palm Springs, CA, USA.

Authors: Patrick Robertson, Michael Angermann, Mohammed Khider, German Aerospace Center (DLR)

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

Pesented at IEEE/ION PLANS 2010, May 2010, Palm Springs, CA, USA.

Authors: Patrick Robertson, Michael Angermann, Mohammed Khider, German Aerospace Center (DLR)

Anzeige
Anzeige

Weitere Verwandte Inhalte

Diashows für Sie (20)

Ähnlich wie Improving Simultaneous Localization and Mapping for Pedestrian Navigation and Automatic Mapping of Buildings by using Online Human-Based Feature Labeling (15)

Anzeige

Aktuellste (20)

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

  1. 1. 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.
  2. 2. 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!
  3. 3. 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)
  4. 4. 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)
  5. 5. A Person Processes Numerous Visual Inputs
  6. 6. 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.
  7. 7. FootSLAM Human Odometry Data Processed with a Particle Filter 5 meters
  8. 8. Human-Recognizable Places
  9. 9. 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 An Example of Placestamps
  10. 10. The PlaceSLAM Dynamic Bayesian Network (DBN) P U Zu E Int Vis L, M “Visual impression - what the person sees“ Intention “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 Time k P U Zu E Int Vis ZL A Placestamp Place identifier seen ZL A Odometry Error states Actual step taken (pose change vector) Pose (= location, orientation)
  11. 11. 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!
  12. 12. Illustration of Proposal Function 1 dmin Particle position dmin If particle is closer than dmin to some existing place(s) then choose the closest place
  13. 13. Illustration of Proposal Function 2 dmin If particle is further than dmin from all existing places then choose a new place at the particle‘s current position Particle position New place proposed to be here
  14. 14. 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 dmin Closest is within dmin Multiply weight by PL 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 Noplacestamp reported Perform for all Np Particles:
  15. 15. Weight update If particle i revisited a place: If particle marked a new place: r cancels out and pL accounts for places being sparse
  16. 16. Intuitive Illustration Place
  17. 17. Intuitive Illustration: Perfect Assoc. Place
  18. 18. Intuitive Illustration: Unknown Assoc. Place dmin
  19. 19. 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
  20. 20. Resulting Maps Large conference Table Canteen
  21. 21. Improvement of Positioning Accuracy
  22. 22. Video
  23. 23. 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
  24. 24. Thank you! Movies and papers: http://www.kn-s.dlr.de/indoornav/
  25. 25. Intuitive Illustration Place

×