MCMC Occupancy Grid Mapping with a Data-Driven Patch Prior

Abstract

Occupancy grids have been widely used for mapping with mobile robots for several decades. Occupancy grids discretize the analog environment and seek to determine the occupancy probability of each cell. More recent occupancy grid mapping algorithms have shown the advantage of capturing cell correlations in the measurement model and the posterior. By estimating the probability of a given map as opposed to a cell, these algorithms have been able to better capture the occupancy probability of cells in the map. The advantage of incorporating data-driven prior probabilities in occupancy grid mapping is explored. A form of Markov Chain Monte Carlo (MCMC) known as Gibbs sampling allows us to sample maps from the full posterior. Previous research has sampled the occupancy probability of each cell, but this paper extends that work to sample a larger patch of cells and highlights the benefit of obtaining the prior for each patch from real maps.