Several robotic applications imply motion in complex and dynamic environments. Occupancy Grids model the surrounding environment by a grid composed of a finite number of cells. The probability whether a cell is occupied or empty is computed and updated iteratively based on sensor measurements by considering their uncertainty through probabilistic models. Even if Occupancy Grids have been widely used in the state-of-the-art, the relation between the cell size, the sensor precision and the inverse sensor model is usually neglected. In this paper, we propose a methodology to build the inverse probabilistic model for single-target sensors. The proposed approach is then applied to a LiDAR in order to evaluate the impact of the variation in the sensor precision and the grid resolution on the inverse sensor model. Based on this study, we finally propose a procedure that allows to choose the suitable grid resolution for obtaining a desired maximum occupancy probability in the inverse sensor model.
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