NDLI: Mobile robot outdoor localization using planar beacons and visual improved odometry

Content Provider	IEEE Xplore Digital Library
Author	Moreira, M.A.G. Machado, H.N. Mendonca, C.F.C. Pereira, G.A.S.
Copyright Year	2007
Description	Author affiliation: Univ. Fed. de Minas Gerais, Belo Horizonte (Moreira, M.A.G.; Machado, H.N.; Mendonca, C.F.C.; Pereira, G.A.S.)
Abstract	This paper presents experimental results on the localization of a mobile robot equipped with relative frequent and absolute infrequent sensors. The relative sensors used are two: a wheel based odometry and a visual based odometry. The absolute sensor is a vision based landmark detector that computes the pose of the robot relative to a pre-mapped visual beacon. This would be a simple sensor fusion problem, which could be solved using standard recursive estimators, if we would not have considered two extra characteristics of the beacon detector: (1) since we assume a monocular vision system and a planar visual mark, the localization problem presents up to four possible solutions; and (2) the frequency that the robot meets a visual mark is very low (0.01 Hz or less). To consider these characteristics, we propose the use of a particle filter with a very precise prediction step (obtained by combining the two odometry sensors available) and a correction step that considers the multi-modal characteristic of the data. Besides, the sensor fusion algorithm, the paper also describes the development of the visual sensors used in the localization process.
Starting Page	2468
Ending Page	2473
File Size	851564
Page Count	6
File Format	PDF
ISBN	9781424409112
DOI	10.1109/IROS.2007.4399384
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mobile robots Sensor phenomena and characterization Sensor fusion Detectors Robot sensing systems Robot vision systems Frequency estimation Wheels Computer vision Recursive estimation
Content Type	Text
Resource Type	Article

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