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ICRA
2002
IEEE

Weighted Range Sensor Matching Algorithms for Mobile Robot Displacement Estimation

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Weighted Range Sensor Matching Algorithms for Mobile Robot Displacement Estimation
This paper introduces a “weighted” matching algorithm to estimate a robot’s planar displacement by matching twodimensional 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 la...
Samuel T. Pfister, Kristopher L. Kriechbaum, Sterg
Added 15 Jul 2010
Updated 15 Jul 2010
Type Conference
Year 2002
Where ICRA
Authors Samuel T. Pfister, Kristopher L. Kriechbaum, Stergios I. Roumeliotis, Joel W. Burdick
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