Integrating the whole cost-curve of stereo into occupancy grids

Extensive literature has been written on occupancy grid mapping for different sensors. When stereo vision is applied to the occupancy grid framework it is common, however, to use sensor models that were originally conceived for other sensors such as sonar. Although sonar provides a distance to the nearest obstacle for several directions, stereo has confidence measures available for each distance along each direction. The common approach is to take the highest-confidence distance as the correct one, but such an approach disregards mismatch errors inherent to stereo. In this work, stereo confidence measures of the whole sensed space are explicitly integrated into 3D grids using a new occupancy grid formulation. Confidence measures themselves are used to model uncertainty and their parameters are computed automatically in a maximum likelihood approach. The proposed methodology was evaluated in both simulation and a real-world outdoor dataset which is publicly available. Mapping performance of our approach was compared with a traditional approach and shown to achieve less errors in the reconstruction.