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WiSlam presentation

  1. 1. WiSLAM: improving FootSLAM with WiFi IPIN 2011 – Guimaraes, 21/9/2011 Dr. Patrick Robertson, German Aerospace Center (DLR, Germany) Luigi Bruno, PhD. Student (Univ. of Salerno, Italy) Folie 1 Vortrag > Autor > Dokumentname > Datum
  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! Folie 2 Vortrag > Autor > Dokumentname > Datum
  3. 3. What about SLAM for Humans? Human pedestrians differ from robots for the information available No access to “sensorial” data No access to path planning and execution Some sensors are not likely (e.g. cameras, lasers,..) Exploitable information 'Odometry' can be measured using inertial sensors Proximity to some “places” (e.g. RFID) Distance from some “places” (e.g. RSS meas. in WiFi) Our central assumption: The pedestrian is able to actively control motion without violating physical constraints (i.e. walls, etc) Folie 3 Vortrag > Autor > Dokumentname > Datum
  4. 4. Raw NavShoe Odometry Results Algorithm: Extended Kalman Filter with Zero Velocity 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 Folie 4 Vortrag > Autor > Dokumentname > Datum
  5. 5. FootSLAM 1/2 FootSLAM (Robertson et alii, 2009) employs only inertial sensors Corrects the heading errors by estimating the floor map Bayesian approach: Particle Filter Folie 5 Vortrag > Autor > Dokumentname > Datum
  6. 6. FootSLAM 2/2 A model for the MAP Area divided into hexagons The ‘Map’ is the set of transition probabilities ‘Probabilistic’ Map Convergence: Each particle is a hypothesis for both user’s trajectory and Map The Map confirmed by next measurements wins Loops required Folie 6 Vortrag > Autor > Dokumentname > Datum
  7. 7. WiSLAM WiSLAM is a new algorithm performing SLAM for pedestrians using Inertial measurements Received Signal Strength (RSS) from WiFi APs RSS Propagation model P (d ) = h − 20α log10 d d0 RSS Likelihood Gaussian in dB User ‘donut’ in the 2D space Lognormal in range d Uniform in angle AP Folie 7 Vortrag > Autor > Dokumentname > Datum
  8. 8. Model validation 1/2 Propagation model validation Likelihood function validation Autocorrelation of the noise Folie 8 Vortrag > Autor > Dokumentname > Datum
  9. 9. Model validation 1/2 WiFi standard was not designed for positioning.. Rx connected to ‘red’ AP Rx not connected to any AP Folie 9 Vortrag > Autor > Dokumentname > Datum
  10. 10. WiSLAM – the idea Same concept: The WiFi Map confirmed by next measurements wins but now the Map consists in the APs’ positions updated at each measurement, no need for loops! Folie 10 Vortrag > Autor > Dokumentname > Datum
  11. 11. DBN FootSLAM WiSLAM WiFi Map APs’ positions APs’ emitted power P(d ) = h − 20α log10 d d0 Folie 11 Vortrag > Autor > Dokumentname > Datum
  12. 12. Intuitive explanation of WiSLAM WiSLAM lets particles, or hypotheses, explore the state space of odometry errors, floor and WLAN maps In this way, every particle is trying a slightly “differently bent piece of wire”, as well as a configuration for the WiFi APs. Particles are weighted independently by their “compatibility” with their individual FootSLAM (floor) map their individual WiFi map optional sensor readings, such as GPS, magnetometer We can show that this is optimal in the Bayesian sense! Folie 12 Vortrag > Autor > Dokumentname > Datum
  13. 13. Bayesian formulation 1/2 Particle Filter: Posterior PDF The weight due to the WiFi part is Folie 13 Vortrag > Autor > Dokumentname > Datum
  14. 14. Bayesian formulation 2/2 Central function Weight WiFi map How to compute and update it for all particles and efficiently? Number of parameters growing with time Approximation needed Folie 14 Vortrag > Autor > Dokumentname > Datum
  15. 15. Simplified WiSLAM High values concentrated at the intersections of the ‘donuts’: Gaussian Mixture Model Deal with few parameters Peaks update at new measurements Implemented by plain formulas Computationally efficient Folie 15 Vortrag > Autor > Dokumentname > Datum
  16. 16. Experiments and Results Measurement data taken from a pedestrian wearing a foot mounted IMU and holding a laptop WiFi receiver embedded in the laptop: Link 5100 2 WiFi APs (Cisco AiroNet 1130, Apple Airport Extreme A1301) Scenario: Indoor only: first floor of the building TE01, two datasets Experiments: Only Mapping WiSLAM without FootSLAM weights WiSLAM + FootSLAM Folie 16 Vortrag > Autor > Dokumentname > Datum
  17. 17. Mapping k=1 k=5 AP position PDF k=11 Folie 17 Vortrag > Autor > Dokumentname > Datum
  18. 18. Dataset 1 No SLAM, only ZUPT algorithm on IMU’s measurements WiSLAM, only weights from WiFi map Folie 18 Vortrag > Autor > Dokumentname > Datum
  19. 19. DS 1 - WiSLAM without FootSLAM 50000 particles 7 power hypotheses Estimations 5 db spaced RSS std dev. 5 dB Max 10 peaks x GMM APs Positions RSS sampling time: 3 s Performance metrics: 8.8% of walls crossed Folie 19 Vortrag > Autor > Dokumentname > Datum
  20. 20. DS 1 - WiSLAM + FootSLAM 20000 particles 9 power hypotheses 3 db spaced RSS std dev. 5 dB Max 14 peaks x GMM RSS sampling time: 2 s Performance metrics: 1.3% of walls crossed Folie 20 Vortrag > Autor > Dokumentname > Datum
  21. 21. DS 2 - WiSLAM without FootSLAM 20000 particles 9 power hypotheses 3 db spaced RSS std dev. 5 dB Max 14 peaks x GMM RSS sampling time: 2 s Performance metrics: 5.9% of walls crossed Folie 21 Vortrag > Autor > Dokumentname > Datum
  22. 22. DS 2 - WiSLAM + FootSLAM 20000 particles 9 power hypotheses 3 db spaced RSS std dev. 5 dB Max 14 peaks x GMM RSS sampling time: 2 s Performance metrics: 0.5% of walls crossed Folie 22 Vortrag > Autor > Dokumentname > Datum
  23. 23. Complexity remarks Processing complexity: • Linear in the number of peaks and hypotheses • Linear in the number of Aps • Linear in time Memory requirements • Linear in the number of peaks and hypotheses • Linear in the number of Aps • Constant in time Folie 23 Vortrag > Autor > Dokumentname > Datum
  24. 24. Concluding Notes RSS measurements from WiFi contain information useful to SLAM WiSLAM (like all forms of SLAM) is inherently invariant to rotation, translation and scale Bayesian approach used to merge IMU’s and RSS measurements Experiments show the convergence of the algorithm Good results obtained also when the floor map is not employed in the weighting of the particles Our future work: Map building with multiple users Folie 24 Vortrag > Autor > Dokumentname > Datum
  25. 25. Thank you! Contacts: Dr. Patrick Robertson Luigi Bruno, PhD Student Email: patrick.robertson@dlr.de Email: lbruno@unisa.it Institute of Communications and Navigation, Department of Information and Electrical Engineering, German Aerospace Center (DLR), University of Salerno, D-82230, Wessling via Ponte don Melillo I-84084 Fisciano, Germany Italy Folie 25 Vortrag > Autor > Dokumentname > Datum