Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of $\sim$2 particles/m$^2$ is required to achieve 100% convergence success for large-scale ($\sim$100,000 m$^2$), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software.

翻译：在预构建地图中实现车辆精确定位是任何自动驾驶导航系统的核心。本研究证明，标准SIR采样与基于拒绝的最优采样均适用于利用3D激光雷达原始点云、无需特征检测的高效（10至20毫秒）实时位姿追踪。鉴于此类传感器捕获的丰富信息，我们系统性地统计分析了实际所需点云数量，以在效率与定位精度之间达到最优平衡。此外，针对恶劣初始条件（如城市峡谷中GPS信号弱）下的初始化问题，我们确定了确保收敛所需的最优粒子滤波参数设置。研究发现：对于VLP-16扫描仪，在输入点云上采用100至200倍的降采样因子可在显著降低计算成本的同时，几乎不造成定位精度损失。同时，在大规模（约100,000平方米）户外全局定位中，无需GPS或磁场传感器辅助，需达到约2个粒子/平方米的初始密度方可实现100%收敛成功率。所有实现均已作为开源软件发布。