Base Algorithms of Environment Maps and Efficient Occupancy Grid Mapping on Embedded GPUs

An accurate model of the environment is essential for future Advanced Driver Assistance Systems (ADASs). To generate such a model, an enormous amount of data has to be fused and processed. Todays Electronic Control Units (ECUs) struggle to provide enough computing power for those future tasks. To overcome these shortcomings, new architectures, like embedded Graphics Processing Units (GPUs), have to be introduced. For future ADASs, also sensors with a higher accuracy have to be used. In this paper, we analyze common base algorithms of environment maps based on the example of the occupancy grid map. We show from which sensor resolution it is rational to use an (embedded) GPU and which speedup can be achieved compared to a Central Processing Unit (CPU) implementation. A second contribution is a novel method to parallelize an occupancy grid map on a GPU, which is computed from the sensor values of a lidar scanner with several layers. We evaluate our introduced algorithm with real driving data collected on the autobahn.