The Bayesian occupation filter

Perception of and reasoning about dynamic environments is pertinent for mobile robotics and still constitutes one of the major challenges. To work in these environments, the mobile robot must perceive the environment with sensors; measurements are uncertain and normally treated within the estimation framework. Such an approach enables the mobile robot to model the dynamic environment and follow the evolution of its environment. With an internal representation of the environment, the robot is thus able to perform reasoning and make predictions to accomplish its tasks successfully. Systems for tracking the evolution of the environment have traditionally been a major component in robotics. Industries are now beginning to express interest in such technologies. One particular example is the application within the automotive industry for adaptive cruise control [Coué et al., 2002], where the challenge is to reduce road accidents by using better collision detection systems. The major requirement of such a system is a robust tracking system. Most of the existing target-tracking algorithms use an object-based representation of the environment. However, these existing techniques must explicitly consider data association and occlusion. In view of these problems, a gridbased framework, the Bayesian occupancy filter (BOF) [Coué et al., 2002, 2003], has been proposed.