Bayesian generalized kernel inference for occupancy map prediction

We consider the problem of building accurate and descriptive 3D occupancy maps of an environment from sparse and noisy range sensor data. We seek to accomplish this task by constructing a predictive model online and inferring the occupancy probability of regions we have not directly observed. We propose a novel algorithm leveraging recent advances in data structures for mapping, sparse kernels, and Bayesian nonparametric inference. The resulting inference model has several desirable properties in comparison to existing methods, including speed of computation, the ability to be recursively updated without approximation, and consistency between batch and online inference. The method also reverts to the use of a specified prior state when insufficient relevant training data exist to predict the occupancy probability of a query point, a property which is attractive for motion planning and exploration applications with mobile robots.