Three-dimensional environment perception is one of the most important tasks for an autonomous vehicle. Map-based approaches play a fundamental role in the representation of vehicle surroundings, allowing several perception features, such as obstacle detection or road classification. However, benchmarks available in literature do not allow to evaluate the accuracy of these discrete representations, focusing only on the results downstream the maps. The proposed system uses a stochastic approach to evaluate a generic discrete representation of a three-dimensional world. The evaluation process consists in comparing a local perceived representation with the corresponding previously computed ground truth. The ground truth is automatically generated exploiting either accurate depth sensing and precise localization information. A test case is proposed, using stereo vision data and Digital Elevation Maps.
['Francesco Valenti', 'Francesca Ghidini', 'Marco Patander', 'Alberto Broggi']
A ground truth building approach for evaluation of grid based discretization techniques in automotive scenarios