A navigation system for an autonomous mobile robot is described, which is basically probabilistic. The sensing of the environment is made through a rangefinder, each measurement updating an occupancy grid, which is the internal representation of the robot's environment. The vehicle uses only local information for path planning and obstacle avoidance, searching for the path of minimum cost to the goal. The robot's position and orientation estimates are corrected after each scan of the environment by making a least square fit between the internal map and the set of observed points. Results of computer simulations are shown.