摘要
针对现有进化点云拼接算法未使用点云色彩信息的局限性,提出一种基于色彩信息的自适应进化点云拼接算法。使用随机采样与色彩特征点相结合的方式对输入点云进行采样,通过最小化包含色彩约束的点对距离中值建立目标函数,利用自适应进化算法求解两片点云间的最优空间变换,实现点云的有效拼接。对四幅彩色点云进行拼接实验,结果表明,与仅使用空间信息的自适应进化点云拼接算法和其他两种较新的进化拼接算法相比,所提算法在保证同等拼接精度的情况下能够有效缩短拼接时间。
Traditional evolutionary point cloud registration methods often not using the color information in the models. To overcome the defect,this paper introduced a point cloud registration method based on self-adaptive evolutionary optimization algorithm and color information. It subsampled the input point clouds by extracting the color feature points and randomly chose points,it utilized the median of all pairs of color constrained points as the object function. At last,it used the self-adaptive evolutionary optimization algorithm to get optimal solution. The registration experiments on four colorized point clouds show that,compared with the evolutionary point cloud registration methods only spatial information use in and two state-of-the-art registration methods,the method significantly shorten the processing time while achieving similar registration precision.
作者
邹力
葛宝臻
陈雷
Zou Li;Ge Baozhen;Chen Lei (School of Precision Instruments & Opto-Electronics Engineering,Tianjin University,Tianjin 300072,China;Key Laboratory of Opto-Electronics Information & Technical Science of Ministry of Education,Tianjin University,Tianjin 300072,China;School of Information Engineering,Tianjin University of Commerce,Tianjin 300134,China)
出处
《计算机应用研究》
CSCD
北大核心
2019年第1期303-307,共5页
Application Research of Computers
基金
国家自然科学基金重点资助项目(61535008)
关键词
彩色点云拼接
自适应进化算法
特征点提取
color point cloud registration
self-adaptive evolution optimization algorithm
feature points extraction
