Bayesian Hilbert Maps for Dynamic Continuous Occupancy Mapping
Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:458-471, 2017.
Hilbert mapping is an efficient technique for building continuous occupancy maps from depth sensors such as LiDAR in static environments. However, to make the map adaptable to dynamic environments, its parameters need to be learned automatically. In this paper, we take a variational Bayesian approach to this problem, thus eliminating the regularization term typically adjusted heuristically. We extend the proposed model to learn long-term occupancy maps in dynamic environments in a sequential fashion, demonstrating the power of kernel methods to capture abstract nonlinear patterns and Bayesian learning to construct sophisticated models. Experiments conducted in environments with moving vehicles show that the proposed approach has a significant speed improvement over the state-of-the-art techniques and maintain a similar or better accuracy. We also discuss the robustness against occlusions and various theoretical and empirical aspects of building long-term dynamic occupancy maps.