Simultaneous Localization and Mapping for Pedestrians using Distortions of the Local Magnetic Field Intensity in Large Indoor Environments

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Simultaneous Localization and Mapping for Pedestrians using Distortions of the Local Magnetic Field Intensity in Large Indoor Environments

Patrick Robertson1, Martin Frassl1, Michael Angermann1, Marek Doniec2, Brian J. Julian2, Maria Garcia Puyol1, Mohammed Khider1, Michael Lichtenstern1, and Luigi Bruno1

1 Institute of Communications and Navigation, German Aerospace Center (DLR)2 Computer Science and Artificial Intelligence Laboratory, MIT

Presented at IPIN 2013,

October 2013, Montbeliard,

France


Magnetic Localization in Buildings?

- Listing- Second Level

- Third Level- Fourth Level

- Fifth Level

Photo source: The Salt Lake Tribune

“One person’s noise

is another’s signal” –

Brad Parkinson


Human Step Measurement: Odometry


- Zero Velocity

Updates during still

phase!

- Pioneering work:

Foxlin, 2005- Proposition: Use a foot mounted IMU with co-

located magnetometer.

“Close to the ground lots of signal”

Unaided Human Odometry Exhibits Drift


Magnetic Localisation in Buildings

- Goldenberg, Geomagnetic navigation beyond the magnetic compass, 2006

- Haverinen, Kemppainen, A global self-localization technique utilizing local

anomalies of the ambient magnetic field, 2009

- Zhang, Martin, Robotic mapping assisted by local magnetic field anomalies, 2011

- Gozick et al, Magnetic maps for indoor navigation, 2011

- Riehle et al., Indoor waypoint navigation via magnetic anomalies, 2011

- Kim et al., Indoor positioning system using geomagnetic anomalies for

smartphones, 2012

- Le Grand, Thrun, 3-axis magnetic field mapping / fusion for indoor localization, 2012

- Angermann et al., Characterizing Magnetic Field in Buildings, 2012

- Frassl et al., Magnetic maps of indoor environments for precise localization of

legged and non-legged locomotion, 2013


Simultaneous Localization and Mapping (SLAM)

- Simultaneously Localizing and has been done in robotics since ~25 years - Smith, Self, Cheeseman (1990) - Leonard, Durrant-Whyte (1991)

- SLAM requires exteroceptive sensor to “see” the world- FootSLAM (2009) – Learns and uses a building layout while walking

around in it (odometry only)- PlaceSLAM (human labeling) (2010)- Robotic SLAM using the local magnetic field: Haverinen,Vallivaara et

al. (2009-2010)- ActionSLAM, Hardegger et al. (IPIN 2012, IPIN 2013)- Wifi-SLAM (Ferris, 2007) and WiSLAM (Bruno 2011, IPIN 2013)- SignalSLAM (Mirowski et al., IPIN 2013)


Objectives for this Paper

- Magnetic SLAM for “free roaming” pedestrians in large

environments

- Foot mounted IMU / co-located magnetometer

- Bayesian Derivation

- Manageable complexity (real-time capability)

- Confirm it works in 3D

- Verification with accurate ground-truth to confirm low-decimeter

accuracy aspirations


Basis for Derivation: Dynamic Bayesian Network


- B: Magnetic environment map

- E: Error states of the odometry

- ZU: Measured odometry Step

- ZB: Measured Magnetic Field

- U: Actual step taken

(pose change vector)

- P: Pose

- Int, Vis: Human processing

- M: Physical environment map

“FootSLAM”

“MagSLAM”

MagSLAM Implementation with a Sequential Monte Carlo Filter

- FastSLAM factorization (Montemerlo et al. 2002)

- Proposal Function:

- propose odometry error process

- particles are propagated by odometry and their individual odometry

error

- Likelihood Functions:

- FootSLAM weighting (hexagonal edge crossing counters)

- and MagSLAM predictive posterior

- MagSLAM & FootSLAM per-particle mapping: Update each cell’s statistics


Magnetic Field Strength at Ground Level: Finding a Robust Map Representation


Source: M. Frassl et al., “Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion,” in Intelligent Robots and Systems (IROS), Tokyo, Japan, Nov. 2013.

Hierarchical Magnetic Field Map Composed of Regular Grids


Mapping: Spatial Binning of Measurements


1: 65 mT

2: 63 mT

3: 94 mT

4: 74 mT

1: 74 mT

2: 84 mT

1: 84 mT

5: 84 mT

3: 87 mT

6: 87 mT 7: 42 mT

All we save are sample mean,

variance and N

Localization: Conjugate Bayesian Analysis

- Assume the measurements in each bin follow a normal distribution with

constant but unknown mean m and precision l- Assume a prior distribution on m and l that jointly follow the conjugate

prior of a normal distribution, i.e., a normal-gamma distribution:- NG(m, l | m0, k0, a0, b0)

- Then: Given the sufficient statistics of independent magnetic field

measurements taken within a bin, the posterior mean and precision also

jointly follow a normal-gamma distribution- The resulting posterior predictive of a new measurement is a student T-

distribution:

> IPIN 2013 > Patrick Robertsonwww.DLR.de • Chart 14

Evolution of the Posterior Predictive - No Measurements


Prio

r P

DF

Evolution of the Posterior Predictive


Pos

terio

r P

DF

His

togr

am p

er b

in

Evolution of the Posterior Predictive


Pos

terio

r P

DF

His

togr

am p

er b

in

Results


Video of Walking in Holodeck


Comparison of MagSLAM Map with Ground Truth


Not visited by

the human

Position Error Evolution


Average error over 4 similar datasets: ~15 cm

MagSLAM Map of a Residential Flat


Error at the Reference = Revisited Starting Location


Error for MagSLAM alone: ~13 cm

Example in Large Office Buildings - 1


met

ers

meters

MagSLAM from IPIN Demo


met

ers

Example in Large Office Buildings - 2


3D Video

Chart 27 > IPIN 2013 > Patrick Robertson

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Simultaneous Localization and Mapping for Pedestrians using Distortions of the Local Magnetic Field Intensity in Large Indoor Environments