WiSlam presentation

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)

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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!

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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)
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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

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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

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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

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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

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

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Model validation 1/2
WiFi standard was not designed for positioning..

Rx connected to ‘red’ AP Rx not connected to any AP
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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!

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DBN
FootSLAM

WiSLAM

WiFi Map

APs’ positions
APs’ emitted power

P(d ) = h − 20α log10 d
d0

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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!

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Bayesian formulation 1/2
Particle Filter: Posterior PDF

The weight due to the WiFi part is

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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

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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

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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

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Mapping

k=1

k=5
AP position PDF

k=11

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Dataset 1
No SLAM, only ZUPT algorithm on IMU’s measurements
WiSLAM, only weights from WiFi map

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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

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DS 1 - WiSLAM + FootSLAM

20000 particles

9 power hypotheses
3 db spaced

RSS std dev. 5 dB

Max 14 peaks x GMM



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DS 2 - WiSLAM without FootSLAM

20000 particles

9 power hypotheses
3 db spaced

RSS std dev. 5 dB

Max 14 peaks x GMM



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DS 2 - WiSLAM + FootSLAM

20000 particles

9 power hypotheses
3 db spaced

RSS std dev. 5 dB

Max 14 peaks x GMM



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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

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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
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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

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