Environment perception using grid occupancy estimation with belief functions

Grid map offers a useful representation of the perceived world for mobile robotics navigation. It will play a major role for the safety (obstacle avoidance) of next generations of terrestrial vehicles, as well as for future autonomous navigation systems. In a grid map, the occupancy state of each cell represents a small piece of information of the surrounding area of the vehicle. The state of each cell must be estimated from sensors measurements and classified in order to get a complete and precise perception of the dynamic environment where the vehicle moves. So far, the estimation and the grid map updating have been done using fusion techniques based on the probabilistic framework, or on the classical belief function framework thanks to an inverse model of the sensors and Dempster-Shafer rule of combination. Recently we have shown that PCR6 rule (Proportional Conflict Redistribution rule #6) proposed in DSmT (Dezert-Smarandache Theory) did improve substantially the quality of grid map with respect to other techniques, especially when the quality of available information is low, and when the sources of information appear as conflicting. In this paper, we go further and we analyze the performance of the improved version of PCR6 with Zhang's degree of intersection. We will show through different realistic scenarios (based on a LIDAR sensor) the benefit of using this new rule of combination in a practical application.