GOGICP: A Real-time Gaussian Octree-based GICP Method for Faster Point Cloud Registration

摘要

Point cloud registration algorithms are crucial for robot localization based on prior maps. Classical methods often struggle to achieve both high accuracy and real-time performance simultaneously, as these two goals can conflict. This limitation impedes the application of point cloud registration in high-speed scenarios, such as the LiDAR localization of fast-moving robots, where both precision and real-time capabilities are essential. This paper proposes a real-time Gaussian Octree-based Generalized Iterative Closest Point (GICP) method, which leverages the censoring properties of the Gaussian Octree to expedite the nearest neighbor search and incorporates an incremental calculation of the Gaussian distribution to hasten the tree-building process. Experimental results show that the proposed method can achieve a 61.8% improvement in speed over GICP while maintaining accuracy comparable to that of GICP. The average registration accuracy is up to 5 cm, with an angular error of less than 0.2°, and the average registration time for a single frame is 77 ms, which satisfies the demands of practical application scenarios.