Integation Methods of Model-Free Features for 3D Tracking
A number of approaches for 3D pose tracking have been recently introduced, most of them utilizing an edge (wireframe) model of the target. However, the use of an edge model has significant problems in complex scenes due to background, occlusions, and multiple responses. Integration of model-free information has been recently proposed to decrease these problems.
In this paper, we propose two integration methods for model-free point features to enhance the robustness and to increase the performance of real-time model-based tracking. The relative pose change between frames is estimated using an optimization approach. This allows the pose change to be integrated very efficiently in a Kalman filter. Our first approach estimates the pose change in a least squares sense while the second one uses M-estimators to decrease the effect of outliers. Experiments are presented which demonstrate that the approaches are superior in performance to earlier approaches.
KeywordsInterest Point Direct Integration Basic Optimization Basic Optimization Approach Measurement Covariance Matrix
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