This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce memory consumption, we utilize a sparse hash table for storing hierarchical 3D voxel maps. To improve the processing cost of BBS in 3D space, we propose an efficient roto-translational space branching. Furthermore, we devise a batched BnB algorithm to fully leverage GPU parallel processing. Through experiments in simulated and real environments, we demonstrated that the 3D-BBS enabled accurate global localization with only a 3D LiDAR scan roughly aligned in the gravity direction and a 3D pre-built map. This method required only 878 msec on average to perform global localization and outperformed state-of-the-art global registration methods in terms of accuracy and processing speed.
@article{arxiv.2310.10023,
title = {3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm},
author = {Koki Aoki and Kenji Koide and Shuji Oishi and Masashi Yokozuka and Atsuhiko Banno and Junichi Meguro},
journal= {arXiv preprint arXiv:2310.10023},
year = {2024}
}
Comments
IEEE International Conference on Robotics and Automation (ICRA2024)