Bayesian occupancy grid mapping via an exact inverse sensor model

Abstract

Occupancy grid maps are spatial representations of environments, where the space of interest is decomposed into a number of cells that are considered either occupied or free. This paper focuses on occupancy grid mapping, which is to estimate the probability of occupancy for each cell based on range measurements from a known location. For a given probabilistic model of a range sensor, we propose a computationally efficient method to obtain an exact inverse sensor model, and it is utilized to construct a probabilistic mapping algorithm according to the Bayesian framework. Compared with the existing occupancy grid mapping techniques that rely on approximate, heuristic inverse sensor models, the proposed approach yields substantially more accurate maps for the same set of measurements. These are illustrated by numerical examples and experiments.