Iterative-Reweighting-Based Robust Iterative-Closest-Point Method  

作　　者：ZHANG Jianlin ZHOU Xuejun YANG Ming 张建林;周学军;杨明(Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China;Key Laboratory of System Control and Information Processing of Ministry of Education,Shanghai 200240,China;Noblelift Intelligent Equipment Co.,Ltd.,Huzhou 313100,Zhejiang,China)

机构地区：[1]Department of Automation,Shanghai Jiao Tong University,Shanghai 200240,China [2]Key Laboratory of System Control and Information Processing of Ministry of Education,Shanghai 200240,China [3]Noblelift Intelligent Equipment Co.,Ltd.,Huzhou 313100,Zhejiang,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2021年第5期739-746,共8页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China(No.U1764264)。

摘　　要：In point cloud registration applications,noise and poor initial conditions lead to many false matches.False matches significantly degrade registration accuracy and speed.A penalty function is adopted in many robust point-to-point registration methods to suppress the influence of false matches.However,after applying a penalty function,problems cannot be solved in their analytical forms based on the introduction of nonlinearity.Therefore,most existing methods adopt the descending method.In this paper,a novel iterative-reweighting-based method is proposed to overcome the limitations of existing methods.The proposed method iteratively solves the eigenvectors of a four-dimensional matrix,whereas the calculation of the descending method relies on solving an eight-dimensional matrix.Therefore,the proposed method can achieve increased computational efficiency.The proposed method was validated on simulated noise corruption data,and the results reveal that it obtains higher efficiency and precision than existing methods,particularly under very noisy conditions.Experimental results for the KITTI dataset demonstrate that the proposed method can be used in real-time localization processes with high accuracy and good efficiency.

关 键 词：point cloud registration iterative reweighting iterative closest-point(ICP) robust localization 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]