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.

Occupancy grids have been a popular mapping technique in mobile robotics for nearly 30 years. Occupancy grids offer a discrete representation of the world and seek to determine the occupancy probability of each cell. Traditional occupancy grid mapping methods make two assumptions for computational efficiency and it has been shown that the full posterior is computationally intractable for real-world mapping applications without these assumptions. The two assumptions result in tuning parameters that control the information gained from each distance measurement. In this paper, several tuning parameters found in the literature are optimized against the full posterior in 1D. In addition, this paper presents a new parameterization of the update function that outperforms existing methods in terms of capturing residual uncertainty. Capturing the residual uncertainty better estimates the position of obstacles and prevents under- and over-confidence in both the occupied and unoccupied cells. The paper concludes by showing that the new update function better captures the residual uncertainty in each cell when compared to an offline mapping method for realistic 2D simulations.

Occupancy grids have been widely used for mapping with mobile robots for nearly 30 years. Occupancy grids discretize the analog environment and seek to determine the occupancy probability of each cell. Traditional occupancy grid mapping methods make two assumptions for computational efficiency and it has been shown that the full posterior is computationally intractable without these assumptions. This paper employs a form of Markov Chain Monte Carlo (MCMC) known as Gibbs sampling to sample from the full posterior. By drawing many samples, we are able to capture the full posterior, which more accurately represents the uncertainty in the map due to sensor measurement error. The MCMC method is shown to compute the full posterior in a 1D toy example, and it is shown to be computationally tractable, though not online, for realistic 2D simulations.

Mobile robots have been using two-dimensional discrete occupancy grid maps for more than 25 years to represent a continuous environment. Occupancy grids discretize the environment into a grid of cells and seek to determine the occupancy of each cell. These maps are generally used to determine the degree to which each cell is occupied.