An evidential sensor model for Velodyne scan grids

For the development of driving assistance systems and autonomous vehicles, a reference perception equipment including navigable space determination and obstacles detection is a key issue. The Velodyne sensor which provides high definition and omnidirectional information can be used for this purpose. Nevertheless, when scanning around the vehicle, uncertainty necessarily arises due to unperceived areas and noisy measurements. This paper proposes an inverse evidential model for the Velodyne in order to exploit its measurements in a 2D occupancy grid mapping framework. The evidential sensor model interprets the data acquired from the Velodyne and successively maps it to a Carthesian evidential grid using a fusion process based on the least commitment principle to guarantee information integrity. Experimental results prove that this approach can handle efficiently the uncertainties of the sensor and thus a highly reliable local reference map near the vehicle can be built for every timestamped perception system that needs evaluation or calibration.