A Robust and Precise LiDAR-Inertial-GPS Odometry and Mapping Method for Large-Scale Environment

In this article, we propose a system that can fuse inertial measurement unit (IMU), light detection and ranging (LiDAR), and intermittent global positioning system (GPS) measurements to achieve high precision localization and mapping in large-scale environment. The iterated error-state Kalman filter is used to fuse IMU and LiDAR measurements to estimate relative motion information quickly. Based on the factor graph, LiDAR, GPS measurements, and loop closure are transformed into constraints for joint optimization to construct global map. A real-time GPS outlier detection method based on the IMU preintegration theory and residual chi-square test is designed. In addition, the use of robust kernel is supported to implicitly reduce the impact of undetected GPS outliers on the system. A LiDAR-based loop-closure detector is designed, which can search for a pair of point clouds with similar geometric information. Importantly, the performance of the proposed method is compared with state-of-the-art methods through a series of experiments. The experimental results show that the method is robust and accurate to complete the task of localization and mapping in large-scale environment.