We propose K-Closest Points (KCP), an efficient and effective laser scan matching approach inspired by LOAM and TEASER++. The efficiency of KCP comes from a feature point extraction approach utilizing the multi-scale curvature and a heuristic matching method based on the k-closest points. The effectiveness of KCP comes from the integration of the feature point matching approach and the maximum clique pruning. We compare KCP against well-known scan matching approaches on synthetic and real-world LiDAR data (nuScenes dataset). In the synthetic data experiment, KCP-TEASER reaches a state-of-the-art root-mean-square transformation error (0.006, m, 0.014) with average computational time 49 ms. In the real-world data experiment, KCP-TEASER achieves an average error of (0.018 m, 0.101) with average computational time 77 ms. This shows its efficiency and effectiveness in real-world scenarios. Through theoretic derivation and empirical experiments, we also reveal the outlier correspondence penetration issue of the maximum clique pruning that it may still contain outlier correspondences.
- Computer vision for transportation
- Range sensing