| Authors |
Kempter Thomas, Wendel Andreas, Bischof Horst |
| Appeared in |
Proceedings of the Computer Vision Winter Workshop (CVWW) |
| Date |
2012 |
| Abstract |
We present a novel model- and keypoint-based pose estimation approach which extends state-
of-the-art visual Simultaneous Localization And Mapping (SLAM) by using prior knowledge about
the geometry of a single object in the scene. Using keypoint-based localization, our object of interest is generally tracked by its surroundings. Additionally, we incorporate the knowledge about the object’s geometry to initialize the SLAM algorithm metrically, we use the model to refine the object’s pose to increase accuracy, and finally we use a purely model-based tracking component to overcome loss of tracking when the keypoint-based approach lacks distinctive features. Experiments show an improvement of the mean translational error from 6.1 cm to 1.7 cm for solid objects, and from 6.9 cm to 2.6 cm for wiry objects. Furthermore, by the use of model-based tracking we reduce the number of pose estimation failures by more than 20%.
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| Link |
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