Point cloud registration algorithm based on 3D shape context features

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.