A segmented, graph-optimized segmentation that positions and plots at the same time

Abstract

Aiming at the process of simultaneous positioning and mapping (SLAM) of traditional lidar, the lidar scanning frame only matches the current submap, and with the continuous increase of submaps, the error continues to accumulate, resulting in problems such as the global map cannot be closed loop, and the global map distortion is proposed. According to the IMU angular velocity data, when the IMU angular displacement changes greatly, the system will reduce the trust value of the IMU data at this time according to the dynamic weight distribution coefficient, and use this moment as the starting point of the segment, reconstruct the local subgraph, and add loopback constraints to it. The next frame of radar data is used as the initial pose to construct the local submap again, cycle through it in turn, and finally optimize each local submap to build a complete map by means of global map optimization. After experimental verification, the algorithm can effectively improve the distortion of the global map caused by the excessive rotation angle, which is of practical significance.