Variational Bayesian Point Set Registration

Point set registration presents unique significance in Lidar-based intelligent vehicle localization and mapping. It involves registering point sets of the same scene observed from different positions by determining their relative spatial transformation. However, due to the noise and outliers in the point sets and initial misalignment, existing methods suffer from the issues of low accuracy or large computational cost. In this paper, we propose a novel Bayesian state space model to describe the sequential point registration problem. Specifically, we specify the transformations to be the latent states and further assume that they vary smoothly across time. The point clouds are represented as Gaussian mixture models that change accordingly with the transformation. We develop a stochastic variational Bayesian inference algorithm to learning the distributions of the transformation, which automatically strikes a balance between mapping every two consecutive point clouds and the temporal smoothness of the transformation. Experimental results based synthetic data show that the proposed variational Bayesian point set registration (VB-PSR) algorithm achieves higher accuracy with comparable or less time and resources, in comparison with the state-of-the-art methods.