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

• 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
• DOI: 10.1109/JPROC.2012.2189785

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.