K-Closest Points and Maximum Clique Pruning for Efficient and Effective 3-D Laser Scan Matching

Abstract

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(0.006\,\mathrm{m}, 0.014^\circ)$</tex-math></inline-formula> with average computational time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$49\mathrm{ms}$</tex-math></inline-formula> . In the real-world data experiment, KCP-TEASER achieves an average error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(0.018\,\mathrm{m}, 0.101^\circ)$</tex-math></inline-formula> with average computational time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$77\,\mathrm{ms}$</tex-math></inline-formula> . 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.