Stereo vision specific models for particle filter-based SLAM

This work addresses the SLAM problem for stereo vision systems under the unified formulation of particle filter methods. In contrast to most existing approaches to visual SLAM, the present method does not rely on restrictive smooth camera motion models, but on computing incremental 6D pose differences from the image flow through a probabilistic visual odometry method. Moreover, our observation model, which considers both the 3D positions and the SIFT descriptors of the landmarks, avoids explicit data association between the observations and the map by marginalizing the observation likelihood over all the possible associations. We have experimentally validated our research with two experiments in indoor scenarios.