3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm

• Published in: 2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 1796 - 1802
• Main Authors: Aoki, Koki, Koide, Kenji, Oishi, Shuji, Yokozuka, Masashi, Banno, Atsuhiko, Meguro, Junichi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.05.2024
• Subjects: Accuracy; Graphics processing units; Laser radar; Location awareness; Memory management; Point cloud compression; Three-dimensional displays
• DOI: 10.1109/ICRA57147.2024.10610810

This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branchand-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.