Scan Context 3D Lidar Inertial Odometry via Iterated ESKF and Incremental K-Dimensional Tree

This paper focused on a 3D lidar inertial odometry algorithm framework that improves the LeGO-LOAM by constructing a new back-end optimization algorithm. In comparison with the LeGO-LOAM, the feature extraction and image projection processes are still the same. Two-step Levenberg Marquardt was replaced with an iterated ESKF method in the lidar odometry to produce a better initial pose for the robots, and the k-d tree method in the lidar mapping is replaced with the ikd-Tree method to ensure high performance mapping process in real time. In the loop closure, a scan context search method is added to better correct the algorithm’s final trajectory. We compare the improved algorithm with LeGO-LOAM and the two other methods, LIO-SAM and A-LOAM, using three datasets gathered from the Mulran dataset with different large-scale outdoor scenes. We show that the improved algorithm achieves similar or better accuracy in real-time than the other three algorithms.

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