Efficient GPU-based Construction of Occupancy Grids Using several Laser Range-finders

Building occupancy grids (OGs) in order to model the surrounding environment of a vehicle implies to fusion occupancy information provided by the different embedded sensors in the same grid. The principal difficulty comes from the fact that each can have a different resolution, but also that the resolution of some sensors varies with the location in the field of view. In this article we present a new efficient approach to this issue based upon a graphical processor unit (GPU). In that perspective, we explain why the problem of switching coordinate systems is an instance of the texture mapping problem in computer graphics. We also present an exact algorithm in order to evaluate the accuracy of such a device, which is not precisely known due to the several approximations made by the hardware. To validate our method, the results with GPU are also compared to results obtained through the exact approach and the GPU precision is shown to be good enough for robotic applications. Therefore we describe a whole and general calculus architecture to build occupancy grids for any kind of range-finder with a graphical processor unit (GPU). And we present computational time results that can allow to compute occupancy grids for 50 sensors at frame rate even for a very fine grid.