激光与光电子学进展, 2020, 57 (4): 041008, 网络出版: 2020-02-20
基于体素下采样和关键点提取的点云自动配准 下载: 2015次
Automatic Point Cloud Registration Based on Voxel Downsampling and Key Point Extraction
图像处理 体素 关键点 特征提取 点云配准 随机采样一致性算法 image processing voxels key points feature extraction point cloud registration random sample consensus algorithm
摘要
针对最近点迭代算法(ICP)在大数据点云下配准效率低及对配准点云初始位置依赖性强的缺点,提出了一种基于快速点云粗配准与 ICP 算法相结合的方法。根据体素对原始点云进行下采样,结合法向量特征提取关键点,使用快速点特征直方图(FPFH)算法描述关键点;根据局部邻域内的关键点匹配对的向量夹角特性进一步对匹配点对进行精简;对精简后的关键点对集使用随机采样一致性算法(RANSAC)获取内点最多的变换参数,从而完成点云粗配准;最后在粗配准点云的基础上使用 ICP 算法完成精确配准。实验结果表明,本算法在高密集点云上的配准效率和精度均有所提高。
Abstract
The disadvantages of the nearest point iterative algorithm (ICP) are low registration efficiency in the big-data point cloud and strong dependence on the initial position of the registration point cloud. To overcome these disadvantages, this study proposes a method that combines the fast point cloud coarse registration method with the ICP algorithm. First, the original point cloud is sampled according to the voxel, and after extracting the key points with the normal vector feature, it is described by the fast point feature histogram (FPFH) algorithm. Subsequently, according to the vector angle feature of the key matching pair in the local neighborhood, the matching point pair is further simplified. Next, the reduced key sequence pair set is used to obtain the transformation parameter with the most interior points using the random sampling consensus algorithm (RANSAC), thereby completing the point cloud coarse registration. Finally, accurate registration is performed using the ICP algorithm on the basis of the point cloud coarse registration. Experimental results show that the registration efficiency and accuracy of the algorithm are improved for high-density point clouds.
张彬, 熊传兵. 基于体素下采样和关键点提取的点云自动配准[J]. 激光与光电子学进展, 2020, 57(4): 041008. Bin Zhang, Chuanbing Xiong. Automatic Point Cloud Registration Based on Voxel Downsampling and Key Point Extraction[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041008.