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.

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在三维空间中的计算开销，提出了一种高效旋转-平移空间分支策略；此外，我们设计了批处理BnB算法以充分利用GPU并行处理能力。通过在模拟及真实环境中的实验证明，仅需粗略对齐重力方向的三维激光雷达扫描数据与预构建三维地图，3D-BBS即可实现精确全局定位。本方法平均仅需878毫秒完成全局定位，且在精度与处理速度上均优于现有最先进的全局配准方法。