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

www.DLR.de • Chart 1

> IPIN 2013 > Patrick Robertson

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 colocated 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
“MagSLAM”
- 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”

www.DLR.de • Chart 10 > IPIN 2013 > Patrick Robertson

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
5: 84 mT
6: 87 mT
7: 42 mT
1: 74 mT
2: 84 mT
3: 87 mT
1: 84 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 Tdistribution:


Prior PDF

Evolution of the Posterior Predictive
- No Measurements


Histogram per bin

Posterior PDF



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


meters

Example in Large Office Buildings - 1

meters


meters

MagSLAM from IPIN Demo


Example in Large Office Buildings - 2

Chart 27


3D Video


Thanks!
Questions?
Videos on our website

www.kn-s.dlr.de/indoornav/magnetic.html

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

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

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

Similar to Simultaneous Localization and Mapping for Pedestrians using Distortions of the Local Magnetic Field Intensity in Large Indoor Environments (20)

Recently uploaded

Recently uploaded (20)

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