Advanced absolute orientation algorithm based on unit quaternion on alignment

Abstract

The main objective of this paper is to develop 3D point cloud alignment technique by using an improved absolute orientation algorithm based on unit quaternion. In this method, Scale Invariant Features Transform (SIFT) is used to find corresponding feature points, and Random Sample Consensus (RANSAC) is used as robust estimator to remove false matches in the point cloud group. The unit quaternion solution is employed for initial registration. After the initial registration of point clouds, this cannot meet the requirements of registration accuracy. Therefore, we need to achieve accurate registration on the basis of initial registration. The Iterative Closest Point (ICP) algorithm is one of the widely-used methods to cope with 3D registration. However, ICP is vulnerable to outliers and missing data, which severely compromises its performance. In this paper, Sparse L p -norm based ICP algorithm is developed to achieve precise registration. Experimental results verified accuracy of the presented algorithm in point cloud registration.