An improved RANSAC registration algorithm based on region covariance descriptor

Abstract

Point clouds registration is one of the key parts in 3D model reconstruction. Random Sample Consensus (RANSAC) is a typical algorithm for coarse registration, which can provide initial values for accurate registration methods such as ICP. In this paper, we propose an improved RANSAC algorithm based on 3D region covariance descriptor(RC RANSAC). Region covariance descriptor of each point in a down sampled point cloud is established. The region covariance descriptor extracts the statistical information of neighborhood for each point. Region covariance descriptor has more significant distinction than Euclidean distance adopted by classical RANSAC and another improved RANSAC algorithm, called M RANSAC. The accuracy can be improved by significant distinction. Additionally, the region covariance descriptor with compact structure is efficient in time. Experimental results on 3D point clouds registration show that the RCRANSAC has superior performance to RANSAC and MRANSAC in computational complexity and accuracy.