Fast Point Cloud Registration Algorithm Based on 3DNPFH Descriptor

Abstract

Although researchers have investigated a variety of approaches to the development of three-dimensional (3D) point cloud matching algorithms, the results have been limited by low accuracy and slow speed when registering large numbers of point cloud data. To address this problem, a new fast point cloud registration algorithm based on a 3D neighborhood point feature histogram (3DNPFH) descriptor is proposed for fast point cloud registration. With a 3DNPFH, the 3D key-point locations are first transformed into a new 3D coordinate system, and the key points generated from similar 3D surfaces are then close to each other in the newly generated space. Subsequently, a neighborhood point feature histogram (NPFH) was designed to encode neighborhood information by combining the normal vectors, curvature, and distance features of a point cloud, thus forming a 3DNPFH (3D + NPFH). The descriptor searches radially for 3D key point locations in the new 3D coordinate system, reducing the search coordinate system for the corresponding point pairs. The “NPFH” descriptor is then coarsely aligned using the random sample consensus (RANSAC) algorithm. Ex-periment results show that the algorithm is fast and maintains high alignment accuracy on several popular benchmark datasets, as well as our own data.