Reducing mismatching under time-pressure by reasoning about visibility and occlusion

Three methods are explored which help indicate whether feature points are potentially visible or occluded in the matching phase of the keyframe-based real-time visual SLAM system. The first derives a measure of potential visibility from the angular proximity to keyframes in which they were observed and globally adjusted, and preferentially selects those with high visibility when tracking the camera position between keyframes. It is found that sorting and selecting features within image bins spread over the image improves tracking stability. The second method automatically recognizes and locates 3D polyhedral objects alongside the point map, and uses them to determine occlusion. The third method uses the map points themselves to grow surfaces. The performance of each is tested on live and recorded sequences.