FootSLAM: Pedestrian Simultaneous Localization and Mapping Without Exteroceptive Sensors-Hitchhiking on Human Perception and Cognition

In this paper, we describe FootSLAM, a Bayesian estimation approach that achieves simultaneous localization and mapping for pedestrians. FootSLAM uses odometry obtained with foot-mounted inertial sensors. Whereas existing approaches to infrastructure-less pedestrian position determination are either...

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Bibliographic Details
Published in:Proceedings of the IEEE Vol. 100; no. Special Centennial Issue; pp. 1840 - 1848
Main Authors: Angermann, Michael, Robertson, Patrick
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.05.2012
Institute of Electrical and Electronics Engineers
Subjects:
Applied sciences
Bayesian methods
Detection, estimation, filtering, equalization, prediction
Electrical engineering. Electrical power engineering
Electronic equipment and fabrication. Passive components, printed wiring boards, connectics
Electronics
Exact sciences and technology
FeetSLAM
FootSLAM
Human factors
Information, signal and communications theory
Legged locomotion
Mobile radio mobility management
Navigation
odometry
Optical telecommunications
pedestrian navigation
Power electronics, power supplies
Sensors
Signal and communications theory
Signal, noise
Simultaneous localization and mapping
simultaneous localization and mapping (SLAM)
Telecommunications
Telecommunications and information theory
Visualization
Positioning
Parameter estimation
odometry
Pedestrian
Cognition
Mapping
FootSLAM
Accuracy
Infinite medium
pedestrian navigation
Bayes network
FeetSLAM
Localization
Growth of error
Bayes estimation
Prior information
Exteroceptive sensor
Measurement sensor
Power electronics
simultaneous localization and mapping (SLAM)
Long term
Subjective evaluation
Power device
Lidar
Bayes methods
Smartphone
ISSN:0018-9219, 1558-2256
Online Access:Get full text
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Description
Summary:In this paper, we describe FootSLAM, a Bayesian estimation approach that achieves simultaneous localization and mapping for pedestrians. FootSLAM uses odometry obtained with foot-mounted inertial sensors. Whereas existing approaches to infrastructure-less pedestrian position determination are either subject to unbounded growth of positioning error, or require either a priori map information, or exteroceptive sensors, such as cameras or light detection and ranging (LIDARs), FootSLAM achieves long-term error stability solely based on inertial sensor measurements. An analysis of the problem based on a dynamic Bayesian network (DBN) model reveals that this surprising result becomes possible by effectively hitchhiking on human perception and cognition. Two extensions to FootSLAM, namely, PlaceSLAM, for incorporating additional measurements or user provided hints, and FeetSLAM, for automated collaborative mapping, are discussed. Experimental data that validate FootSLAM and its extensions are presented. It is foreseeable that the sensors and processing power of future devices such as smartphones are likely to suffice to position the bearer with the same accuracy that FootSLAM achieves with foot-mounted sensors already today.
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2012.2189785