環境変化に強いリアルタイム3D LiDAR自己位置推定
Real-Time, Environmentally-Robust 3D LiDAR Localization
3D LiDARとGPSで作成した地図を小領域に分割してレジストレーションし、複数のオドメトリを融合することで、変化する環境でも高精度・高頻度に自己位置を推定する手法を提案した。
著者: Yilong Zhu, Bohuan Xue, Linwei Zheng, Huaiyang Huang, Ming Liu, Rui Fan
分類: cs.RO
原文アブストラクト
Localization, or position fixing, is an important problem in robotics research. In this paper, we propose a novel approach for long-term localization in a changing environment using 3D LiDAR. We first create the map of a real environment using GPS and LiDAR. Then, we divide the map into several small parts as the targets for cloud registration, which can not only improve the robustness but also reduce the registration time. PointLocalization allows us to fuse different kinds of odometers, which can optimize the accuracy and frequency of localization results. We evaluate our algorithm on an unmanned ground vehicle (UGV) using LiDAR and a wheel encoder, and obtain the localization results at more than 20 Hz after fusion. The algorithm can also localize the UGV in a 180-degree field of view (FOV). Using an outdated map captured six months ago, this algorithm shows great robustness, and the test results show that it can achieve an accuracy of 10 cm. PointLocalization has been tested for a period of more than six months in a crowded factory and has operated successfully over a distance of more than 2000 km.
