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 and best-first search strategy. 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 and a 3D pre-built map. This method required only 878 msec on average to perform global localization and outperformed state-of-the-art feature-matching-based global localization methods in terms of accuracy and processing speed.

翻译：本文提出了一种精确且快速的三维全局定位方法3D-BBS，该方法扩展了现有的基于分支定界（BnB）的二维扫描匹配（BBS）算法。为降低内存消耗，我们利用稀疏哈希表存储分层三维体素地图；为改善BBS算法在三维空间中的处理成本，我们提出了一种高效的旋转平移空间分支与最佳优先搜索策略；此外，我们设计了一种批量化的BnB算法以充分利用GPU并行处理能力。通过仿真环境与真实环境实验，我们证明了3D-BBS仅需一次三维激光雷达扫描和预先构建的三维地图即可实现精确的全局定位。该方法完成全局定位平均仅需878毫秒，在精度和处理速度方面均优于基于特征匹配的最先进全局定位方法。