Currently, visual odometry and LIDAR odometry are performing well in pose estimation in some typical environments, but they still cannot recover the localization state at high speed or reduce accumulated drifts. In order to solve these problems, we propose a novel LIDAR-based localization framework, which achieves high accuracy and provides robust localization in 3D pointcloud maps with information of multi-sensors. The system integrates global information with LIDAR-based odometry to optimize the localization state. To improve robustness and enable fast resumption of localization, this paper uses offline pointcloud maps for prior knowledge and presents a novel registration method to speed up the convergence rate. The algorithm is tested on various maps of different data sets and has higher robustness and accuracy than other localization algorithms.

翻译：当前，视觉里程计与LIDAR里程计在某些典型环境中表现出良好的位姿估计性能，但仍无法在高速运动下恢复定位状态或有效消除累积漂移。为解决这些问题，本文提出一种新颖的基于LIDAR的定位框架，该框架通过融合多传感器信息，在三维点云地图中实现高精度且鲁棒的定位。该系统将全局信息与基于LIDAR的里程计相结合，以优化定位状态。为提升系统鲁棒性并实现快速重定位，本文采用离线点云地图作为先验知识，并提出一种创新的配准方法以加速收敛速率。该算法在多个数据集的不同地图上进行测试，相较于其他定位算法展现出更高的鲁棒性与精度。