Dynamic Scaling Factors of Covariances for Accurate 3D Normal Distributions Transform Registration

Abstract

Distribution-to-distribution normal distributions transform (NDT-D2D) is one of the fast point set registrations. Since the normal distributions transform (NDT) is a set of normal distributions generated by discrete and regular cells, local minima of the objective function is an issue of NDT-D2D. Also, we found that the objective function based on L2 distance between distributions has a negative correlation with rotational alignment. To overcome the problems, we present a method using dynamic scaling factors of covariances to improve the accuracy of NDT-D2D. Two scaling factors are defined for the preceding and current NDTs respectively, and they are dynamically varied in each iteration of NDT-D2D. We implemented the proposed method based on conventional NDT-D2D and probabilistic NDT-D2D and compared to the NDT-D2D with fixed scaling factors using KITTI benchmark data set. Also, we experimented estimating odometry with an initial guess as an application of distribution-to-distribution probabilistic NDT (PNDT-D2D) with the proposed method. As a result, the proposed method improves both translational and rotational accuracy of the NDT-D2D and PNDT-D2D.