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http://dx.doi.org/10.7746/jkros.2022.17.3.373

A Fast Correspondence Matching for Iterative Closest Point Algorithm  

Shin, Gunhee (Department of Electrical Engineering, Inha University)
Choi, Jaehee (Department of Mechanical Engineering, Inha University)
Kim, Kwangki (Department of Electrical Engineering, Inha University)
Publication Information
The Journal of Korea Robotics Society / v.17, no.3, 2022 , pp. 373-380 More about this Journal
Abstract
This paper considers a method of fast correspondence matching for iterative closest point (ICP) algorithm. In robotics, the ICP algorithm and its variants have been widely used for pose estimation by finding the translation and rotation that best align two point clouds. In computational perspectives, the main difficulty is to find the correspondence point on the reference point cloud to each observed point. Jump-table-based correspondence matching is one of the methods for reducing computation time. This paper proposes a method that corrects errors in an existing jump-table-based correspondence matching algorithm. The criterion activating the use of jump-table is modified so that the correspondence matching can be applied to the situations, such as point-cloud registration problems with highly curved surfaces, for which the existing correspondence-matching method is non-applicable. For demonstration, both hardware and simulation experiments are performed. In a hardware experiment using Hokuyo-10LX LiDAR sensor, our new algorithm shows 100% correspondence matching accuracy and 88% decrease in computation time. Using the F1TENTH simulator, the proposed algorithm is tested for an autonomous driving scenario with 2D range-bearing point cloud data and also shows 100% correspondence matching accuracy.
Keywords
Scan Matching; Point-Cloud Registration; Iterative Closest Point Methods; Jump Table; LiDAR; Correspondence Matching; Pose Estimation;
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