This paper deals with the development of a localization methodology for autonomous vehicles using only a $3\Dim$ LIDAR sensor. In the context of this paper, localizing a vehicle in a known 3D global map of the environment is essentially to find its global $3\Dim$ pose (position and orientation) within this map. The problem of tracking is then to use sequential LIDAR scan measurement to also estimate other states such as velocity and angular rates, in addition to the global pose of the vehicle. Particle filters are often used in localization and tracking, as in applications of simultaneously localization and mapping. But particle filters become computationally prohibitive with the increase in particles, often required to localize in a large $3\Dim$ map. Further, computing the likelihood of a LIDAR scan for each particle is in itself a computationally expensive task, thus limiting the number of particles that can be used for real time performance. To this end, we propose a hybrid approach that combines the advantages of a particle filter with a global-local scan matching method to better inform the re-sampling stage of the particle filter. Further, we propose to use a pre-computed likelihood grid to speedup the computation of LIDAR scans. Finally, we develop the complete algorithm to extensively leverage parallel processing to achieve near sufficient real-time performance on publicly available KITTI datasets.

翻译：本文研究利用仅配备三维LIDAR传感器的自主车辆定位方法。在本文背景下，车辆在已知三维全局环境地图中的定位本质上是确定其在该地图中的全局三维位姿（位置与朝向）。跟踪问题则需在利用连续LIDAR扫描测量的同时，除车辆全局位姿外，还需估计速度、角速率等其他状态变量。粒子滤波常被用于定位与跟踪（如同时定位与地图构建应用），但随着粒子数量增加（通常需要在大型三维地图中实现定位），其计算量变得难以承受。此外，为每个粒子计算LIDAR扫描似然本身是一项高计算成本任务，这限制了实时性能下可使用的粒子数量。为此，我们提出一种混合方法，结合粒子滤波器与全局-局部扫描匹配方法的优势，以更有效地引导粒子滤波的重采样阶段。进一步地，我们提出使用预计算的似然网格来加速LIDAR扫描的计算。最后，我们开发完整算法，充分利用并行处理能力，在公开KITTI数据集上实现了接近满意的实时性能。