Weighted range sensor matching algorithms for mobile robot displacement estimation
Abstract
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. 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.
Additional Information
© 2002 IEEE. This research has was sponsored in part by a National Science Foundation Engineering Research Center grant (NSF9402726) and NSF ERC-CREST partnership award EEC-9730980. We thank Mash Kumar and Ada Yu for helping to implement the UWLS algorithm, and Aisha Chambliss and Derek Jackson for hardware support.Attached Files
Published - 01014782.pdf
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Additional details
- Eprint ID
- 96686
- Resolver ID
- CaltechAUTHORS:20190625-091700874
- EEC-9402726
- NSF
- EEC-9730980
- NSF
- Created
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2019-06-25Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field