An improved sparse iterative closest point algorithm

ZHOU You，GENG Nan，ZHANG Zhi-yi

（College of Information Engineering,Northwest A & F University,Yangling 712100,China）

Received:2016-04-29 Revised:2016-06-07 Online:2017-10-25 Published:2017-10-25

Abstract:

The sparse iterative closest point algorithm for point cloud with noise points is sensitive to the outliers

contained in the target point cloud, and is inefficient. To solve the problems, we find the corresponding

point-pairs based on neighborhood information to improve the sparse iterative closest point algorithm. The

improved sparse iterative closest point algorithm firstly uses the improved registration based on the PCA to

adjust the position of the two point clouds, and then finds the corresponding point-pairs based on

neighborhood information. Finally we use the alternating direction method of multipliers (ADMM) to get the

optimal transformational matrix for corresponding point-pairs. Experiments on Stanford rabbit and potted

model show that the improved algorithm can handle the outliers contained in the target point cloud, and the

algorithm speed can be increased by 30%.

Key words: registration of point cloud, neighborhood information, sparse iterative closest point algorithm

ZHOU You，GENG Nan，ZHANG Zhi-yi. An improved sparse iterative closest point algorithm[J]. Computer Engineering & Science.

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