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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
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
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
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
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
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
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
Model validation 1/2
Propagation model validation
Likelihood function validation
Autocorrelation of the noise




                                                               Folie 8
                                 Vortrag > Autor > Dokumentname > Datum
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
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
DBN
      FootSLAM

      WiSLAM




        WiFi Map

            APs’ positions
            APs’ emitted power

      P(d ) = h − 20α log10 d
                                                 d0

                                             Folie 11
                 Vortrag > Autor > Dokumentname > Datum
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
Bayesian formulation 1/2
Particle Filter: Posterior PDF




The weight due to the WiFi part is




                                                                 Folie 13
                                     Vortrag > Autor > Dokumentname > Datum
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
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
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
Mapping

                                         k=1




                                         k=5
          AP position PDF




                                         k=11


                                                        Folie 17
                            Vortrag > Autor > Dokumentname > Datum
Dataset 1
 No SLAM, only ZUPT algorithm on IMU’s measurements
 WiSLAM, only weights from WiFi map




                                                                            Folie 18
                                                Vortrag > Autor > Dokumentname > Datum
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
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
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
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
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
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
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

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WiSlam presentation

  • 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 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. 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 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. 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. 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. 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. Model validation 1/2 Propagation model validation Likelihood function validation Autocorrelation of the noise Folie 8 Vortrag > Autor > Dokumentname > Datum
  • 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. 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. 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 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. Bayesian formulation 1/2 Particle Filter: Posterior PDF The weight due to the WiFi part is Folie 13 Vortrag > Autor > Dokumentname > Datum
  • 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. 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 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. 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 s Performance 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 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. 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