Efficient dynamic occupancy grid mapping using non-uniform cell representation

Occupancy grids are widely used in robotics and autonomous systems to create a representation of the environment. Originally designed to map a static environment, recently also dynamic occupancy grids are emerging for the handling of dynamic scenes. However, a major drawback of (dynamic) occupancy grids is the high computational cost (memory and compute) to store and process the information. This is due to the fact, that occupancy grids usually divide the environment in cells of the same size (i.e. uniform grids). As a result, the computational cost increases quadratically with decreasing cell size. Therefore, for many use cases, a trade-off between accuracy (high resolution grid) and distance covered by the grid is required to keep the computational cost in an acceptable range. To overcome this issue we propose in this paper a novel approach for dynamic occupancy grids using non-uniform cell sizes. Our results show, that these non-uniform occupancy grids reduce the numbers of required cells and therefore the computational cost significantly without compromising on the quality of the results.