摘要
迭代最近点(ICP)算法由于其配准精度很高,通常运用于点云的精配准,但其配准精度和迭代收敛性取决于待配准点云的初始位置。提出一种将遗传算法和空间分布熵相融合的空间最优变换矩阵求解算法,以一种新的点云空间位置评价方法——空间分布熵作为遗传算法的目标函数,采用遗传算子指导解的搜索方向,通过新种群的不断迭代使空间分布熵最小,结束后对最优个体解码实现点云的粗配准。实验表明,该算法有效可行,克服了传统方法在有点云缺陷和噪声点时不能提供很好的初始拼接位置的问题,在误差允许的范围内,可以直接实现点云拼接。
The iterative closest point( ICP) algorithm is usually used in the fine registration of point cloud because of its high registration accuracy,but its registration accuracy and iteration convergence depend on the initial registration position of the point cloud to be registered. This paper proposed an algorithm of spatial optimization transformation matrix combining genetic algorithm with spatial distribution entropy,in which used a new point cloud spatial position evaluation method as the objective function of genetic algorithm,it used the genetic operator to guide the solution of search direction. The algorithm achieved the minimum spatial distribution entropy through the new population of iterations,after which the coarse registration of point cloud was achieved by decoding the optimal individuals. The experimental results show that the algorithm is effective and feasible,and it can overcome the problem that traditional method can’t provide a good initial position when the defects and noise exist in the point cloud. The algorithm can directly realize point cloud registration within the acceptable error range.
作者
陈杰
蔡勇
张建生
Chen Jie;Cai Yong;Zhang Jiansheng(School of Manufacturing Science & Engineering,Southwest University of Science & Technology,Mianyang Sichuan 621010,China;Key Laboratory of Testing Technology for Manufacturing Process,Southwest University of Science & Technology,Mianyang Sichuan 621010,China)
出处
《计算机应用研究》
CSCD
北大核心
2019年第1期316-320,共5页
Application Research of Computers
基金
四川省教育厅科研基金资助项目(14ZB0111)
国家教育部共建重点实验室开放基金资助项目(14tdzk06)
关键词
迭代最近点算法
遗传算法
空间分布熵
配准
iterative closest point(ICP) algorithm
genetic algorithm
spatial distribution entropy
registration
