A Multi-Sensor Fusion Localization Algorithm Via Dynamic Target Removal

LVI-SAM is a new lidar-vision-inertial SLAM fusion framework, which realizes the tight coupling between the visual inertial-system and the lidar inertial system through smoothing and mapping, and it can achieve high-precision and robust real-time state estimation and map building. However, in dynamic scenes, due to the interference of dynamic targets, the extracted lidar feature points may produce incorrect constraints when used for pose optimization, which will lead to the degradation of model accuracy and robustness. To solve the performance degradation problem caused by the interference of dynamic targets in dynamic scenes, an improved LVI-SAM algorithm based on the removal of dynamic target point clouds is proposed, that is, in the system's front end, the region growth algorithm is used to effectively segment the front and rear spots in the point cloud data to remove dynamic targets. Then the processed feature points are used for pose estimation of the lidar inertial navigation subsystem, which further improves the constraint of the lidar odometer in the factor map, and improves the accuracy of depth information obtained by the visual-inertial navigation subsystem, so as to improve the overall positioning accuracy of the system. The simulation experiment on M2DGR dataset shows that the improved algorithm improves the accuracy and robustness of pose estimation in dynamic scenes compared with LVI-SAM.