Optimizing online occupancy grid mapping to capture the residual uncertainty

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.