Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization
This work addresses localization challenges for autonomous land vehicles, but it is incremental as it builds on existing multi-sensor fusion methods.
The paper tackles the problem of accumulated drift and high-speed localization failure in land-vehicle odometry by integrating GNSS information with LIDAR-inertial odometry, achieving higher robustness and accuracy compared to other algorithms.
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.