TY - JOUR
T1 - Weighted range sensor matching algorithms for mobile robot displacement estimation
AU - Pfister, Sam T.
AU - Kriechbaum, Kristo L.
AU - Roumeliotis, Stergios I.
AU - Burdick, Joel W.
N1 - Copyright:
Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
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M3 - Conference article
AN - SCOPUS:0036058303
SN - 1050-4729
VL - 2
SP - 1667
EP - 1674
JO - Proceedings - IEEE International Conference on Robotics and Automation
JF - Proceedings - IEEE International Conference on Robotics and Automation
T2 - 2002 IEEE International Conference on Robotics and Automation
Y2 - 11 May 2002 through 15 May 2002
ER -