A tightly-coupled method of lidar-inertial based on complementary filtering

Abstract

In the application of small field angle Lidar for robot SLAM (Simultaneous Localization and Mapping), livox mapping can provide accurate odometer information and point cloud information of the environment with good precision for the robot in a short time. However, over long periods of motion, the laser odometer calculated by livox mapping will produce a large offset, which will reduce the localization accuracy and mapping accuracy of the robot. To overcome above problem, a lidar-inertial navigation odometer compact fusion method based on the idea of complementary filtering is proposed in this paper. By taking advantage of the good static performance of the accelerometer for a long time, the angle value obtained by the gyroscope integration is corrected. In the back-end optimization, the jacobian matrix obtained by the residual calculation of the acceleration in the navigation coordinate system obtained by IMU and the gravitational acceleration is tightly coupled with the jacobian matrix of the lidar residual. Different weights are given to the residual of each part, and the odometer is solved iteratively to further improve the pose accuracy of the whole SLAM system. In this paper, the method is applied to Livox-Mid40. The experimental results show that it can reduce the drift of long time and long distance and improve the accuracy of the system localization and mapping.

MF-LIO: integrating multi-feature LiDAR inertial odometry with FPFH loop closure in SLAM

“…Other researchers have enhanced performance via multisensor fusion. Liu et al [21] proposed a method for tight integration of LiDAR-inertial navigation system odometry based on the concept of complementary filtering. GLIO [22] introduced an integrated estimator for GNSS/LiDAR/IMU that employs factor graph optimization (FGO) for tightly fusing GNSS pseudoranges, Doppler, LiDAR, and IMU measurements.…”