Weighted range sensor matching algorithms for mobile robot displacement estimation

Sam T. Pfister, Kristo L. Kriechbaum, Stergios I. Roumeliotis, Joel W. Burdick

Research output: Contribution to journalConference articlepeer-review

103 Scopus citations

Abstract

This paper introduces a "weighted" matching algorithm to estimate a robot's planar displacement by matching two-dimensional range scans. The influence of each scan point on the overall matching error is weighted according to its uncertainty. We develop uncertainty models that account for effects such as measurement noise, sensor incidence angle, and correspondence error. Based on models of expected sensor uncertainty, our algorithm computes the appropriate weighting for each measurement so as to optimally estimate the displacement between two consecutive poses. By explicitly modeling the various noise sources, we can also calculate the actual covariance of the displacement estimates instead of a statistical approximation of it. A realistic covariance estimate is necessary for further combining the pose displacement estimates with additional odometric and/or inertial measurements within a localization framework [1]. Experiments using a Nomad 200 mobile robot and a Sick LMS-200 laser range finder illustrate that the method is more accurate than prior techniques.

Original languageEnglish (US)
Pages (from-to)1667-1674
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume2
StatePublished - 2002
Event2002 IEEE International Conference on Robotics and Automation - Washington, DC, United States
Duration: May 11 2002May 15 2002

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