[1]马 卫*,李微微.改进人工蜂群算法的点云数据配准优化研究[J].计算机技术与发展,2023,33(06):79-87.[doi:10. 3969 / j. issn. 1673-629X. 2023. 06. 013]
　MA Wei*,LI Wei-wei.Research on Point Clouds Data Registration Optimization Based on Improved Artificial Bee Colony Algorithm[J].,2023,33(06):79-87.[doi:10. 3969 / j. issn. 1673-629X. 2023. 06. 013]
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改进人工蜂群算法的点云数据配准优化研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年06期
页码:
79-87
栏目:
媒体计算
出版日期:
2023-06-10

文章信息/Info

Title:
Research on Point Clouds Data Registration Optimization Based on Improved Artificial Bee Colony Algorithm
文章编号:
1673-629X(2023)06-0079-09
作者:
马 卫* ; 李微微
南京旅游职业学院 酒店管理学院,江苏 南京 211100
Author(s):
MA Wei* ; LI Wei-wei
School of Hotel Management,Nanjing Institute of Tourism and Hospitality,Nanjing 211100,China
关键词:
点云配准; 人工蜂群算法; 二阶振荡; 特征提取; 配准优化
Keywords:
point clouds registration; artificial bee colony algorithm; second-order oscillation; feature extraction; registration optimization
分类号:
TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 06. 013
摘要:
传统的迭代最近点(Iterative Closest Point,ICP) 算法对点云配准产生初始位置敏感,易陷入局部最优,采用群智能优化算法可以有效解决这一问题,但同时会带来计算量较大、搜索效率不高的问题。 为此,该文提出了一种二阶振荡的人工蜂群算法点云配准方法, 通过对输入点云的均匀采样, 并基于邻域半径约束的固有形状特征点 ( Intrinsic ShapeSignature,ISS) 提取简化点云,通过改进
的二阶振荡人工蜂群算法完成对点云较好的初始配准,得到空间变换矩阵参数。最后通过近邻搜索法( k-Dimension tree,k-d tree)加速对应点查找,以提高点云 ICP 精细配准的效率。 通过对不同
初始位置的点云库模型和场景数据进行的配准实验表明,相比传统的配准方法和改进的群智能优化策略,该算法抗噪性好,配准精度高,鲁棒性强。
Abstract:
Traditional iterative closest point ( ICP) algorithm is sensitive to the initial position generated by point cloud registration and isprone to local optimization. Swarm intelligence optimization algorithm can effectively solve this problem,but at the same time,it bringsabout large computational cost and low search efficiency. Therefore, we propose a second - order oscillation point cloud registrationmethod of artificial bee colony. Through uniform sampling of input point clouds and extraction of simplified point clouds based on neighborhood radius constraint intrinsic shape signature ( ISS) ,the improved second-order oscillating artificial bee colony algorithm is used tocomplete the initial registration of the point cloud, and the space transformation matrix parameters are obtained. Finally, the nearestneighbor search method ( k - Dimension tree, k - d tree) is applied to accelerate the corresponding point search to increase ICP fineregistration efficiency. The registration experiments on the point cloud database model and scene data at different initial locations showthat compared with the traditional registration method and the improved swarm intelligent optimization strategy,the proposed algorithmhas excellent anti-noise performance,high registration accuracy and strong robustness.

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更新日期/Last Update: 2023-06-10