Building variable resolution occupancy maps assuming unknown but bounded sensor errors

A widely used technique for constructing two dimensional maps employing range sensors is occupancy grid mapping assuming normal distributed sensor errors. An alternative to the grid map model with its fixed grid cell size are variable resolution grid maps, e.g. quadtrees. In this paper, the authors propose an approach for building occupancy quadtree maps assuming unknown but bounded sensor errors. Therefore, they examine possible types of sensor uncertainty when using laser rangefinders. They show that the majority of possible types of sensor errors can be covered much better by bounded error models than by probabilistic models. Hence, a novel inverse sensor model has been developed that incorporates measurement and pose uncertainty in a mathematical straightforward way using interval analysis. With this model, an approach for incrementally building occupancy quadtree maps is proposed. A first real world experiment has shown the applicability of the approach. Moreover, the authors compare the map with its probabilistic grid map counterpart. The bounded error quadtree has proved to be conservative but more reliable than the conventional probabilistic grid map.