"Stereo vision-specific models for Particle Filter-based SLAM"
, Robotic and Autonomous Systems
.Abstract: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.
2. Source code
This paper made use of the application rbpf-slam
The next video shows: An experiment with a hand-held camera describing a 6 DoF arbitrary trajectory while simultaneously estimating its pose and building a map of the environment.
Results for Stereo-Vision RBPF-SLAM.