基于混合算法的点云配准方法研究  

Research on Point Cloud Registration Method Based on Hybrid Algorithm

作　　者：任伟建[1,2] 高梦宇 高铭泽 张鹏 刘丹 REN Weijian;GAO Mengyu;GAO Mingze;ZHANG Peng;LIU Dan(School of Electrical Information Engineering,Northeast Petroleum University,Daqing 163318,Heilongjiang Province;Key Laboratory of Network and Intelligent Control,Northeast Petroleum University,Heilongjiang Daqing 163318;China Petrileum Piperline Bureau,China Petroleum Pipeline Engineering Corporation Limited,Langfang 065000,China;China National Offshore Oil Corporation Limited,CNOOC Dongfang Petrochemical Corporation Limited,Dongfang 572600,China;D&P Technology Research Institute,Petrochina Liaohe Oil Field Company,Panjin 124010,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318 [2]东北石油大学黑龙江省网络化与智能控制重点实验室,黑龙江大庆163318 [3]中国石油管道局工程有限公司设计分公司,河北廊坊065000 [4]中国海洋石油集团有限公司东方石化有限责任公司,海南东方572600 [5]中国石油天然气股份有限公司辽河油田分公司钻采工艺研究院,辽宁盘锦124010

出　　处：《吉林大学学报（信息科学版）》2019年第4期408-416,共9页Journal of Jilin University（Information Science Edition）

基　　金：国家自然科学基金资助项目(61374127);黑龙江省科学基金资助项目(F2018004)

摘　　要：为解决ICP(Iterative Closest Point)算法对初始点云位置要求高且易陷入局部最优的问题,提出一种新的配准方法。首先遵从优势互补基本思想,结合将人工萤火虫算法和粒子群算法生成自适应人工萤火虫-粒子群算法(AAGPSO:Adaptive Artificial Glowworm-Particle Swarm Optimization),以使算法的收敛速度变快,解的精度得到提高;其次优化迭代最近点算法(ICP),将已改进的AAGPSO算法引入ICP配准算法中进行点云配准,解决ICP算法因点云的初始位置相差较大而陷入局部最优问题,加快整体的配准效率。通过实验对比原始ICP配准方法和改进的配准方法并对其进行误差分析,结果验证了AAGPSO算法在传统ICP算法的基础上提高了配准精度,并且加快了算法收敛速度,改进的配准方法具有明显优越性。In order to solve the problem that the ICP(Iterative Closest Point) algorithm has strict requirements on the initial point cloud position and is easy to be trapped in local optimum,a new registration method is proposed. Firstly,based on the idea of complementary advantages,the artificial glowworm algorithm and particle swarm algorithm are combined to propose an AAGPSO(Adaptive Artificial Glowworm-Particle Swarm Optimization),accelerating the convergence speed of the algorithm and improving the accuracy of the solution.Secondly,for a different initial location of the point cloud,the improved AAGPSO algorithm is introduced into the ICP registration algorithm making ICP algorithm optimized,and solving the local optimum problem of the ICP algorithm. The improved algorithm accelerates the overall registration efficiency. Finally,experimental data are used to compare the original ICP registration method with the improved registration method,and the error analysis is carried out. The AAGPSO algorithm improves the registration accuracy,accelerates the algorithm convergence speed.

关 键 词：人工萤火虫-粒子群优化算法 点云配准 ICP算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]