Mobile robot localization based on Ultra-Wide-Band ranging: A particle filter approach

Authors:
J. González;J. L. Blanco;C. Galindo;A. Ortiz-de-Galisteo;J. A. Fernández-Madrigal;F. A. Moreno;J. L. Martínez
Affiliations:
Department of System Engineering and Automation, ETSII Campus de Teatinos, University of Málaga, E-29071, Málaga, Spain;Department of System Engineering and Automation, ETSII Campus de Teatinos, University of Málaga, E-29071, Málaga, Spain;Department of System Engineering and Automation, ETSII Campus de Teatinos, University of Málaga, E-29071, Málaga, Spain;Department of System Engineering and Automation, ETSII Campus de Teatinos, University of Málaga, E-29071, Málaga, Spain;Department of System Engineering and Automation, ETSII Campus de Teatinos, University of Málaga, E-29071, Málaga, Spain;Department of System Engineering and Automation, ETSII Campus de Teatinos, University of Málaga, E-29071, Málaga, Spain;Department of System Engineering and Automation, ETSII Campus de Teatinos, University of Málaga, E-29071, Málaga, Spain
Venue:
Robotics and Autonomous Systems
Year:
2009

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Abstract

This article addresses the problem of mobile robot localization using Ultra-Wide-Band (UWB) range measurements. UWB is a radio technology widely used for communications, that is recently receiving increasing attention for positioning applications. In these cases, the position of a mobile transceiver is determined from the distances to a set of fixed, well-localized beacons. Though this is a well-known problem in the scientific literature (the trilateration problem), the peculiarities of UWB range measurements (basically, distance errors and multipath effects) demand a different treatment to other similar solutions, as for example, those based on laser. This work presents a thorough experimental characterization of UWB ranges within a variety of environments and situations. From these experiments, we derive a probabilistic model which is then used by a particle filter to combine different readings from UWB beacons as well as the vehicle odometry. To account for the possible offset error due to multipath effects, the state tracked by the particle filter includes the offset of each beacon in addition to the planar robot pose (x,y,@f), both estimated sequentially. We show navigation results for a robot moving in indoor scenarios covered by three UWB beacons that validate our proposal.