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In simultaneous localization and mapping (SLAM) technique, the presence of cumulative errors in laser odometry positions leads to localization bias or even mapbuilding failure. To address this problem, a laser SLAM method called PA-SLAM based on ground plane constraints and twostep accurate identification of loopback frames is proposed. In the point cloud preprocessing process, the Patchwork++ method is introduced to obtain the ground plane constraints to reduce the localization error caused by invalid feature point matching. Then, accurate six-degree-of-freedom bit positions of the carrier are obtained from Lidar Orientation and Map Construction (LOAM), candidate loopback frames are obtained by searching within a certain radius according to the k-dimensional (K-D) tree data structure, and a Lidar Iris descriptor is introduced to calculate the similarity scores among the candidate frames. Finally, the Fast Generalized Iterative Closest Point (FasterGICP) algorithm is introduced to obtain the loopback position constraints to correct the global pose, so as to obtain more accurate localization information. The proposed method is compared with LeGO-LOAM and SC-LeGO-LOAM algorithms on the KITTI dataset. The experimental results show that the absolute position root-mean-square error (RMSE) of this method is reduced by 45.7% and 48.3% on average relative to the above two algorithms, which plays a significant role in eliminating the cumulative error of the position, and realizes a more robust and accurate localization and 3D map construction.
Zhao et al. (Wed,) studied this question.