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Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 2028414, 20 pages
Research Article

Polar Metric-Weighted Norm-Based Scan Matching for Robot Pose Estimation

1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
2School of Software Engineering, Beijing University of Technology, Beijing 100871, China

Received 17 November 2015; Accepted 6 January 2016

Academic Editor: Daniele Fournier-Prunaret

Copyright © 2016 Guanglei Huo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel point-to-point scan matching approach is proposed to address pose estimation and map building issues of mobile robots. Polar Scan Matching (PSM) and Metric-Based Iterative Closest Point (Mb-ICP) are usually employed for point-to-point scan matching tasks. However, due to the facts that PSM considers the distribution similarity of polar radii in irrelevant region of reference and current scans and Mb-ICP assumes a constant weight in the norm about rotation angle, they may lead to a mismatching of the reference and current scan in real-world scenarios. In order to obtain better match results and accurate estimation of the robot pose, we introduce a new metric rule, Polar Metric-Weighted Norm (PMWN), which takes both rotation and translation into account to match the reference and current scan. For robot pose estimation, the heading rotation angle is estimated by correspondences establishing results and further corrected by an absolute-value function, and then the geometric property of PMWN called projected circle is used to estimate the robot translation. The extensive experiments are conducted to evaluate the performance of PMWN-based approach. The results show that the proposed approach outperforms PSM and Mb-ICP in terms of accuracy, efficiency, and loop closure error of mapping.