多特征关键点的自适应尺度融合特征点云配准  

Adaptive scale fusion feature point cloud registration for multi-feature key points

作　　者：赵迪[1] 赵祖高 程煜林 聂磊[1] Zhao Di;Zhao Zugao;Cheng Yulin;Nie Lei(School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,Chin)

机构地区：[1]湖北工业大学机械工程学院,武汉430068

出　　处：《电子测量技术》2023年第10期68-75,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(51975191)项目资助。

摘　　要：针对点云在噪声、遮挡及相似特征多个干扰条件下容易产生错误配准的问题,提出一种基于多特征的关键点提取算法和自适应尺度的融合特征的点云配准方法。在关键点提取时,同时计算多个特征,使关键点更具描述性和鲁棒性。特征描述时,在自适应尺度的基础上使用FPFH和RoPs特征两种特征分别进行初始配准和错误点对剔除,最终各自得到多个相似的转换矩阵。完成上述求解后,将两者得到的矩阵组成集合进行聚类并对矩阵数最多的类取平均值处理作为最终的结果以完成特征的融合。实验研究表明,在忽略极少数无法具体化的错误配准点的情况下,真实场景下所提算法的RMSE、ATI和ERR分别为0.46 mm,1和0.37;使用数据集测试得到的正确率为99.3%,均表明该算法的精度和鲁棒性较高。Aiming at the problem that point clouds are prone to misregistration under multiple interference conditions of noise,occlusion and similar features,a point cloud registration method based on multi-feature key point extraction algorithm and adaptive scale fusion features was proposed.In keypoint extraction,multiple features are computed simultaneously to make keypoints more descriptive and robust.On the basis of adaptive scale,FPFH and RoPs features are used for initial registration and error point pair elimination,and multiple similar transformation matrices are obtained respectively.After the above solution is completed,the matrix obtained by the two is formed into a set for clustering,and the class with the largest number of matrices is averaged as the final result to complete the feature fusion.The experimental results show that the RMSE,ATI and ERR of the proposed algorithm are 0.46 mm,1 and 0.37 in the actual scenario when a few unrealized error registration points are ignored.The accuracy of the dataset test was 99.3%.It shows that the algorithm has high accuracy and robustness.

关 键 词：关键点 自适应尺度 特征融合 点云配准 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]