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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Vydané v:	Proceedings of the IEEE Ročník 100; číslo Special Centennial Issue; s. 1840 - 1848
Hlavní autori:	Angermann, Michael, Robertson, Patrick
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.05.2012 Institute of Electrical and Electronics Engineers
Predmet:	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
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Popis
Shrnutí:	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