Finding the adequate resolution for grid mapping - Cell sizes locally adapting on-the-fly

For robot mapping occupancy grid maps are the most common representation of the environment. However, most existing algorithms for creating such maps assume a fixed resolution of the grid cells. In this paper we present a novel mapping technique that chooses the resolution of each cell adaptively by merging and splitting cells depending on the measurements. The splitting of the cells is based on a statistical measure that we derive in this paper. In contrast to other approaches the adaption of the resolution is done online during the mapping process itself. Additionally, we introduce the Nd-Tree, a generalization of quadtrees and octrees that allows to subdivide any d-dimensional volume recursively with N^d children per node. Using this data structure our approach can be implemented in a very generic way and allows the creation of 2D, 3D and even higher dimensional maps using the same algorithm. Finally, we show results of our proposed method for 2D and 3D mapping using different kinds of range sensors.