Fast point cloud registration algorithm using multiscale angle features

Abstract

To fulfill the demands of rapid and real-time three-dimensional optical measurement, a fast point cloud registration algorithm using multiscale axis angle features is proposed. The key point is selected based on the mean value of scalar projections of the vectors from the estimated point to the points in the neighborhood on the normal of the estimated point. This method has a small amount of computation and good discriminating ability. A rotation invariant feature is proposed using the angle information calculated based on multiscale coordinate axis. The feature descriptor of a key point is computed using cosines of the angles between corresponding coordinate axes. Using this method, the surface information around key points is obtained sufficiently in three axes directions and it is easy to recognize. The similarity of descriptors is employed to quickly determine the initial correspondences. The rigid spatial distance invariance and clustering selection method are used to make the corresponding relationships more accurate and evenly distributed. Finally, the rotation matrix and translation vector are determined using the method of singular value decomposition. Experimental results show that the proposed algorithm has high precision, fast matching speed, and good antinoise capability.