一种基于三维形状上下文特征的点云配准算法

Point cloud registration algorithm based on 3D shape context features

针对点云配准过程中点云数据量大、配准时间长、配准精度低的问题,提出了一种基于内部形态描述子(intrinsic shape signatures, ISS)和三维形状上下文描述子(3D shape context, 3DSC)的点云配准算法。该方法首先使用体素网格滤波器对点云进行下采样,接着利用ISS算法提取特征点,并通过3DSC进行描述,然后通过改进的随机采样一致性(randon sample consensus, RANSAC)算法进行粗匹配,最后用改进的迭代最近点算法(iterative closest point, ICP)对点云进行精匹配。试验结果表明,与基于ISS+3DSC的三维正态分布变换(normal distribution transformation, NDT)算法和基于采样一致性初始配准(sample consensus initial aligment, SAC-IA)的ICP算法相比,本文算法的配准精度及效率更高,且对于数据量大的点云也有较好的匹配效果。

Aiming at the problems of large amount of point cloud data, long registration time and low registration accuracy in the registration process of point cloud, a point cloud registration algorithm based on intrinsic shape signatures(ISS) and 3D shape context(3DSC) was proposed. Firstly, the down-sampling of point cloud was carried out by using a voxel grid filter. Then, the ISS algorithm was adopted to extract the feature points, which were described by 3DSC, and the rough matching was performed according to the improved random sample consensus(RANSAC) algorithm. Finally, the improved iterative closest point(ICP)algorithm was utilized to accurately match the point cloud. The experimental results show that compared with 3D normal distribution transformation(NDT) algorithm based on ISS+3DSC and ICP algorithm based on sample consensus initial aligment(SAC-IA), the proposed algorithm has higher registration accuracy and efficiency, and also has a better matching effect on point clouds with large data volume.