Journal
IEEE ACCESS
Volume 7, Issue -, Pages 76599-76610Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2921676
Keywords
Iterative closest point; point cloud; autonomous vehicle; ground plane condition
Categories
Funding
- Industrial Convergence Core Technology Development Program (Development of Robot Intelligence Technology for Mobility with Learning Capability Toward Robust and Seamless Indoor and Outdoor Autonomous Navigation) - Ministry of Trade, Industry and Energy (MO [10063172]
- Industry Core Technology Development Project (Development of Artificial Intelligence Robot Autonomous Navigation Technology for Agile Movement in Crowded Space) - MOTIE, South Korea [20005062]
- BK21+
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In this paper, we propose a robust point cloud registration method for ground vehicles. Given the vast developments in the field of autonomous vehicles, the use of point cloud data has increased. The simultaneous localization and mapping (SLAM) algorithm is typically used to generate sophisticated point cloud maps. In the SLAM algorithm, the quality of the map depends on the performance of loop closure algorithms. The iterative closest point (ICP) algorithm is widely used for loop closure of the point cloud. However, the ICP algorithm might not work well for ground vehicles because it was originally developed for 3D reconstruction in computer vision field. Therefore, this paper proposes a method to find robust matching correspondences in the ICP algorithm on ground vehicle conditions. The performance of the proposed method is compared with other conventional methods by using KITTI open datasets. The source code is publicly released on the Github website.
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