In this paper we propose a probabilistic observation model for stereo vision systems which avoids explicit data association between observations and the map by marginalizing the observation likelihood over all the possible associations. We define observations as sets of landmarks composed of their 3D locations, assumed to be normally distributed, and their SIFT descriptors. Our model has been integrated into a particle filter to test its performance in map building and global localization, as illustrated by experiments with a real robot.