An Optimized RANSAC for The Feature Matching of 3D LiDAR Point Cloud

摘要

Accurate feature matching plays important role in subsequent robotic vision tasks. Random sample consensus (RANSAC) is a commonly used estimator for removing the mismatches from the matching results based on 2D or 3D features. The classic RANSAC tries to search for the optimal matches with the maximum number of consistent point pairs. However, this way makes it easy to fall into a local maximum when it is used for fitting the inlier matches from 3D LiDAR point clouds containing lots of similar local features. To solve this problem in the 3D matching of LiDAR point cloud, we propose an optimized RANSAC. The optimized RANSAC estimates the best matches by maximizing the global dispersion under the condition of the truncated number of consistent point pairs. In experiments, we evaluate its performance based on the matching results of LinK3D features on KITTI dataset. The evaluation results show that our method can effectively increase the success rate when LinK3D is executed in challenging scenes (e.g., high-way and forest scenes) and improve its robustness to these scenes.