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WiSlam presentation

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Presentation of the algorithm WiSLAM, approaching Simultaneous Localization and Mapping by merging step measurements from foot mounted IMUs and power measurements in WiFi networks.

Presentation of the algorithm WiSLAM, approaching Simultaneous Localization and Mapping by merging step measurements from foot mounted IMUs and power measurements in WiFi networks.

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  • 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. SLAM in RoboticsSimultaneous Localization and Mapping - identified byrobotics 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. 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. Raw NavShoe Odometry ResultsAlgorithm: 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. FootSLAM 1/2FootSLAM (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. 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 winsLoops required Folie 6 Vortrag > Autor > Dokumentname > Datum
  • 7. WiSLAMWiSLAM is a new algorithm performing SLAM for pedestrians using Inertial measurements Received Signal Strength (RSS) from WiFi APsRSS Propagation model P (d ) = h − 20α log10 d d0RSS 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. Model validation 1/2Propagation model validationLikelihood function validationAutocorrelation of the noise Folie 8 Vortrag > Autor > Dokumentname > Datum
  • 9. Model validation 1/2WiFi standard was not designed for positioning..Rx connected to ‘red’ AP Rx not connected to any AP Folie 9 Vortrag > Autor > Dokumentname > Datum
  • 10. WiSLAM – the ideaSame 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. DBN FootSLAM WiSLAM WiFi Map APs’ positions APs’ emitted power P(d ) = h − 20α log10 d d0 Folie 11 Vortrag > Autor > Dokumentname > Datum
  • 12. Intuitive explanation of WiSLAM WiSLAM lets particles, or hypotheses, explore the state space ofodometry errors, floor and WLAN maps In this way, every particle is trying a slightly “differently bent piece ofwire”, 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. Bayesian formulation 1/2Particle Filter: Posterior PDFThe weight due to the WiFi part is Folie 13 Vortrag > Autor > Dokumentname > Datum
  • 14. Bayesian formulation 2/2Central function Weight WiFi mapHow 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. 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. 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. Mapping k=1 k=5 AP position PDF k=11 Folie 17 Vortrag > Autor > Dokumentname > Datum
  • 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. DS 1 - WiSLAM without FootSLAM 50000 particles 7 power hypothesesEstimations 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. 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. 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 sPerformance metrics: 5.9% of walls crossed Folie 21 Vortrag > Autor > Dokumentname > Datum
  • 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. 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. Concluding NotesRSS measurements from WiFi contain information useful to SLAMWiSLAM (like all forms of SLAM) is inherently invariant to rotation,translation and scaleBayesian approach used to merge IMU’s and RSS measurementsExperiments show the convergence of the algorithmGood results obtained also when the floor map is not employed in theweighting of the particlesOur future work: Map building with multiple users Folie 24 Vortrag > Autor > Dokumentname > Datum
  • 25. Thank you!Contacts:Dr. Patrick Robertson Luigi Bruno, PhD StudentEmail: patrick.robertson@dlr.de Email: lbruno@unisa.itInstitute 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

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