An Improved MbICP Algorithm for Mobile Robot Pose Estimation
Source abstract
Abstract
This paper presents an improved version of the metric-based iterative closest point algorithm to estimate robot poses by matching 2D laser scans with different overlapping percentages. Because of the greatly varied density distribution of realistic point clouds, a resampling method is used to accelerate the iteration process and protect the calculation of the rejection threshold from being distorted by reducing dense but unhelpful points. A new procedure that combines point-to-point and point-to-line metrics is used to determine the correct correspondence between partially overlapping scans, which maintains both efficiency and accuracy. In addition, a rejection threshold that is based on the MAD-from-median method is utilized to discard correspondences with large distances, which are likely to be incorrect. Experiments show that the improved algorithm is more accurate and robust than the standard algorithm with respect to the existence of non-overlapping areas, and testing demonstrates that it is valid in practice.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Advanced Vision and Imaging · Optical measurement and interference techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Resources and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Collaborative Innovation Center of Geo Spatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Funding
National Key R&D Program of China · Scientific and Technological Leading Talent Fund of the National Administration of Surveying, Mapping and Geo-information · Wuhan ‘Yellow Crane Excellence’ (Science and Technology) program
References (31)
Show source-provided references
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