中国机械工程2012，Vol.23Issue(14)：1726-1732,7.
基于核密度估计的点云鲁棒配准算法
Research on Robust Registration Algorithm for Point Clouds Based on Kernel Density Estimation
摘要
Abstract
Aiming at the problems of narrow convergence region and robustness in classical point cloud registration algorithms,a new algorithm for point cloud registration was proposed based on kernel density estimation. A new measure was proposed and used to evaluate the similarity between kernel density functions,which provided a smooth bridge between the Kullback-Liebler divergence and the Euclidean distance. The analytical form measure was derived under rigid constraints. The local maximum phenomenon caused by over-scaled parameter and the drifted maximum phenomenon caused by deficiency-scaled parameter were analyzed based on comparative experiments. And then, the variable-scaled BFGS-quasi Newton method was used to search the optimal parameters. Experimental results show that the method can realize point cloud registration, extending the convergence region, meanwhile, improve the robustness under white noise interferences.关键词
点云配准/核密度估计/测度函数/BFGS拟牛顿法Key words
point cloud registration/kernel density estimation/measure function/BFGS- quasiNewton method分类
信息技术与安全科学引用本文复制引用
林洪彬,刘彬,张玉存..基于核密度估计的点云鲁棒配准算法[J].中国机械工程,2012,23(14):1726-1732,7.基金项目
国家科技重大专项 （）
河北省自然科学基金资助项目 （）
河北省科学技术研究与发展计划资助项目 （）
秦皇岛市科学技术研究与发展计划资助项目 （）
河北省重点实验室开放基金资助项目 （）
