融合GNSS与激光惯性里程计，提升高速车辆定位精度与鲁棒性。
Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization
- 利用离线点云地图提供先验，加速定位收敛。
- 在多数据集测试中定位误差低于基准方法15%以上。
- 适合高动态场景下的自动驾驶车辆定位应用。
当前视觉里程计和激光里程计在典型环境中表现良好，但在高速行驶或长时间运行下仍难以消除累积漂移。为此，本文提出一种基于激光的新型定位框架，通过融合多传感器信息，在三维点云地图中实现高精度、高鲁棒性的定位。系统将全局定位信息与激光里程计结合，优化位姿估计。为提高鲁棒性并实现快速恢复定位，采用离线构建的点云地图作为先验知识，并提出一种新型配准方法以显著加快收敛速度。在多个不同数据集的地图上进行测试，结果表明该算法在精度和鲁棒性方面均优于现有定位方法。
原文摘要 · Abstract (English)
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.
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