3D scan registration using the Normal Distributions Transform with ground segmentation and point cloud clustering

Abstract

The Normal Distributions Transform (NDT) scan registration algorithm models the environment as a set of Gaussian distributions and generates the Gaussians by discretizing the environment into voxels. With the standard approach, the NDT algorithm has a tendency to have poor convergence performance for even modest initial transformation error. In this work, a segmented greedy cluster NDT (SGC-NDT) variant is proposed, which uses natural features in the environment to generate Gaussian clusters for the NDT algorithm. By segmenting the ground plane and clustering the remaining features, the SGC-NDT approach results in a smooth and continuous cost function which guarantees that the optimization will converge. Experiments show that the SGC-NDT algorithm results in scan registrations with higher accuracy and better convergence properties when compared against other state-of-the- art methods for both urban and forested environments.