Abstract
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.
Highlights
Localization and environment mapping are foundational functions of mobile robots
The results show that the proposed approach outperforms Polar Scan Matching (PSM) and Mb-Iterative Closest Points (ICP) in terms of accuracy, efficiency, and loop closure error of mapping
We present a PWMN-based scan matching approach for pose estimation
Summary
The dominant way to implement these functionalities is to use a scan matching approach [1,2,3,4], where the robot pose is iteratively estimated by the established correspondences between reference and current scans. Feature-to-feature approaches adopt the location criteria to match features in both the reference and current scans, while point-to-feature approaches are utilized in geometric distance criteria to match segments in the reference scan with current scan points Both approaches suffer from uncertain or misrecognized features, which inevitably lead to rapid declination of pose estimation accuracy in a real indoor environment. In order to solve problems of Mb-ICP and PSM mentioned above, a novel point-to-point scan matching approach called PMWN is proposed in this paper.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
