| Authors |
Pirker Katrin, Rüther Matthias, Bischof Horst |
| Appeared in |
Proceedings of British Machine Vision Conference 2010 (BMVC) |
| Date |
2010 |
| Abstract |
Simultaneous localization and mapping (SLAM) is a basic prerequisite in autonomous mobile robotics. Most existing visual SLAM approaches either assume a static environment, or simply 'forget' old parts of the map to cope with map size constraints and scene dynamics. We present a novel map representation for sparse visual features. A new 3D point descriptor called Histogram of Oriented Cameras (HOC) encodes anisotropic spatial visibility information and the importance of each three-dimensional landmark. Each feature holds and updates a histogram of the poses of observing cameras. It is hereby able to estimate its probability of occlusion and importance for localization from a given viewpoint. In a series of simulated and real-world experiments we prove that the proposed descriptor allows to cope with dynamic changes in the map, improves localization accuracy and enables reasonable control of the map size. |
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