An improved ICP method using Gaussian curvature

doi:10.7523/j.issn.2095-6134.2019.05.016

Abstract

Abstract: As one of basic topics of computer vision, 3-D point cloud registration has been studied for decades. Among the works, ICP (iterative closest point) is the most well-known algorithm for its simplicity and accuracy. However, due to its iteratively greedy strategy, ICP is time consuming, prone to local minima, and hence susceptible to noise and outliers. In the work, we present a method to improve the performance of ICP in terms of both efficiency and robustness. Firstly, the method estimates Gaussian curvature of each point in the point clouds. Secondly, the method filters out those points which are considered to be trivial points, outliers, and noise. Then, the method applies ICP to the remaining points. The results demonstrate that our method improves both efficiency and resilience against outliers and noise of ICP without causing accuracy degeneration.

Key words: point cloud registration, ICP, Gaussian curvature

CLC Number:

TP391.41

WANG Feipeng, XIAO Jun, WANG Ying, WANG Yunbiao. An improved ICP method using Gaussian curvature[J]. , 2019, 36(5): 702-708.

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