著者

Koki Aoki Tomoya Sato Eijiro Takeuchi Yoshiki Ninomiya Junichi Meguro

出版者

Fuji Technology Press Ltd.

雑誌

Journal of Robotics and Mechatronics (ISSN:09153942)

巻号頁・発行日

vol.35, no.2, pp.435-444, 2023-04-20 (Released:2023-04-20)

参考文献数

23

被引用文献数

3

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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.

著者

Yuki Kitsukawa Tatsuya Minami Yudai Yamazaki Junich Meguro Eijiro Takeuchi Yoshiki Ninomiya Shinpei Kato Masato Edahiro

出版者

Society of Automotive Engineers of Japan, INC

雑誌

International Journal of Automotive Engineering (ISSN:21850984)

巻号頁・発行日

vol.13, no.4, pp.206-213, 2022 (Released:2022-11-22)

参考文献数

18

被引用文献数

2

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ABSTRACT: Ego-vehicle localization is a critical technology in autonomous driving systems, and one of the widely used methods for localization is scan matching between a 3D map and real-time LiDAR scan. This method is known to fail due to factors such as an incorrect initial position and orientation for scan matching. In this paper, we propose a simulator-based localization evaluation framework to verify the robustness of localization. By using a simulator, localization can be evaluated without driving a real vehicle, and can be evaluated by creating disturbances such as traffic jams. Our framework also allows to evaluate the robustness of localization by using multiple particles with random errors of the initial position and orientation for scan matching to simulate dead reckoning errors caused by multiple factors such as road surface conditions and tire diameter. In the evaluation experiments, we confirmed that the robustness of localization can be evaluated by applying this method to factors such as sensor setup, disturbances in the traffic environment, and the amount of 3D features in the environment.