Smoothed normal distribution transform for efficient point cloud registration during space rendezvous

Abstract

Next to the iterative closest point (ICP) algorithm, the normal distribution transform (NDT) algorithm is be- coming a second standard for 3D point cloud registration in mobile robotics. Both methods are effective, however they require a sufficiently good initialization to successfully converge. In particular, the discontinuities in the NDT cost function can lead to difficulties when performing the optimization. In addition, when the size of the point clouds increases, performing the registration in real-time becomes challenging. This work in- troduces a Gaussian smoothing technique of the NDT map, which can be done prior to the registration process. A kd-tree adaptation of the typical octree representation of NDT maps is also proposed. The performance of the modified smoothed NDT (S-NDT) algorithm for pairwise scan registration is assessed on two large-scale outdoor datasets, and compared to the performance of a state-of-the-art ICP implementation. S-NDT is around four times faster and as robust as ICP while reaching similar precision. The algorithm is thereafter applied to the problem of LiDAR tracking of a spacecraft in close-range in the context of space rendezvous, demonstrating the performance and applicability to real-time applications.