S-PTAM: Stereo Parallel Tracking and Mapping.

This paper describes a real-time feature-based stereo SLAM system that is robust and accurate in a wide variety of conditions – indoors, outdoors, with dynamic objects, changing light conditions, fast robot motions and large-scale loops. Our system follows a parallel-tracking-and-mapping strategy: a tracking thread estimates the camera pose at frame rate; and a mapping thread updates a keyframe-based map at a lower frequency. The stereo constraints of our system allow a robust initialization – avoiding the well-known bootstrapping problem in monocular systems–and the recovery of the real scale. Both aspects are essential for its practical use in real robotic systems that interact with the physical world.