JRM Vol.35 No.2 pp. 435-444
doi: 10.20965/jrm.2023.p0435


Error Covariance Estimation of 3D Point Cloud Registration Considering Surrounding Environment

Koki Aoki*1, Tomoya Sato*2, Eijiro Takeuchi*3, Yoshiki Ninomiya*4, and Junichi Meguro*1

*1Meijo University
1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan

*2MAP IV, Inc.
1-1-3 Meieki, Nakamura-ku, Nagoya 450-6627, Japan

*3TIER IV, Inc.
1-12-10 Kitashinagawa, Shinagawa-ku, Tokyo 140-0001, Japan

*4Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

September 30, 2022
February 21, 2023
April 20, 2023
autonomous vehicle, localization, 3D point cloud registration, NDT, degeneration

To realize autonomous vehicle safety, it is important to accurately estimate the vehicle’s pose. As one of the localization techniques, 3D point cloud registration is commonly used. However, pose errors are likely to occur when there are few features in the surrounding environment. Although many studies have been conducted on estimating error distribution of 3D point cloud registration, the real environment is not reflected. This paper presents real-time error covariance estimation in 3D point cloud registration according to the surrounding environment. The proposed method provides multiple initial poses for iterative optimization in the registration method. Using converged poses in multiple searches, the error covariance reflecting the real environment is obtained. However, the initial poses were limited to directions in which the pose error was likely to occur. Hence, the limited search efficiently determined local optima of the registration. In addition, the process was conducted within 10 Hz, which is laser imaging detection and ranging (LiDAR) period; however, the execution time exceeded 100 ms in some places. Therefore, further improvement is necessary.

Overview of our proposed method, e.g., tunnel

Overview of our proposed method, e.g., tunnel

Cite this article as:
K. Aoki, T. Sato, E. Takeuchi, Y. Ninomiya, and J. Meguro, “Error Covariance Estimation of 3D Point Cloud Registration Considering Surrounding Environment,” J. Robot. Mechatron., Vol.35 No.2, pp. 435-444, 2023.
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Last updated on May. 19, 2024