结合GNSS信息与LIDAR-惯性里程计以实现精确的陆地车辆定位
Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization
AI总结：
针对高速和累积漂移导致定位失效的问题，提出一种结合全局信息与LIDAR里程计的多传感器定位框架，利用离线点云先验和新配准方法提升收敛速度，实现了高精度鲁棒的陆地车辆定位。
摘要：
目前，视觉里程计和LIDAR里程计在一些典型环境下的位姿估计中表现良好，但仍无法在高速下恢复定位状态或减少累积漂移。为解决这些问题，我们提出了一种新颖的基于LIDAR的定位框架，该框架实现了高精度，并利用多传感器信息在3D点云图中提供鲁棒的定位。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。为了提高鲁棒性并实现定位的快速恢复，本文使用离线点云图作为先验知识，并提出了一种新颖的配准方法以加快收敛速度。该算法在不同数据集的各种地图上进行了测试，与其他定位算法相比具有更高的鲁棒性和精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
