An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments

Congcong Jin, Jihua Zhu, Yaochen Li, Shaoyi Du, Zhongyu Li, Huimin Lu

Published 2017-06-01Version 1

For the registration of partially overlapping point clouds, this paper proposes an effective approach based on both the hard and soft assignments. Given two initially posed clouds, it firstly establishes the forward correspondence for each point in the data shape and calculates the value of binary variable, which can indicate whether this point correspondence is located in the overlapping areas or not. Then, it establishes the bilateral correspondence and computes bidirectional distances for each point in the overlapping areas. Based on the ratio of bidirectional distances, the exponential function is selected and utilized to calculate the probability value, which can indicate the reliability of the point correspondence. Subsequently, both the values of hard and soft assignments are embedded into the proposed objective function for registration of partially overlapping point clouds and a novel variant of ICP algorithm is proposed to obtain the optimal rigid transformation. The proposed approach can achieve good registration of point clouds, even when their overlap percentage is low. Experimental results tested on public data sets illustrate its superiority over previous approaches on accuracy and robustness.