Dynamic Scaling Factors of Covariances for Accurate 3D Normal Distributions Transform Registration
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.
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)