Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization
Jintao Cheng, Bohuan Xue, Shiyang Chen, Qiuchi Xiang, Xiaoyu Tang
TL;DR
The paper tackles the problem of accurate, drift-resistant land-vehicle localization at high speeds by integrating GNSS, IMU, and LIDAR within a framework that uses an offline 3D pointcloud map. It introduces a novel Dynamic-ICP registration to accelerate convergence for initial localization and relocalization, and combines GNSS velocity, IMU preintegration, and LIDAR odometry to provide robust pose estimates. A constrained GNSS velocity-aided pose adjustment and an integrated LIDAR–Inertial–GNSS navigation scheme further reduce drift and improve global consistency. Experimental results on urban and open environments demonstrate improved accuracy and robustness over baselines, highlighting practical applicability for real-time autonomous navigation in challenging scenarios.
Abstract
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.