Multiple range sensors are essential in mobile robot navigation systems. This introduces the problem of integrating noisy range data from multiple sensors and multiple robot positions into a common description of the environment. We propose a cellular representation called the occupancy grid as a solution to this problem. In this paper, we use occupancy grids to combine range information from sonar and one-dimensional stereo into a two-dimensional map of the vicinity of a robot. Each cell in the map contains a probabilistic estimate of whether it is empty or occupied by an object in the environment. These estimates are obtained from sensor models that describe the uncertainty in the range data. A Bayesian estimation scheme is used to update the existing map with successive range profiles from each sensor. This representation is simple to manipulate, treats different sensors uniformly, and models uncertainty in the sensor data and in the robot position. It also provides a basis for motion planning and creation of higherlevel object descriptions.