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Visual-model-based, real-time 3D pose tracking for autonomous navigation: methodology and experiments

Hans de Ruiter, Beno Benhabib
Computer Integrated Manufacturing Laboratory, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada M5S 3G8
Autonomous Robots, Vol. 25, No. 3. (1 October 2008), pp. 267-286

@article{de2008visual,

   title={Visual-model-based, real-time 3D pose tracking for autonomous navigation: methodology and experiments},

   author={de Ruiter, H. and Benhabib, B.},

   journal={Autonomous Robots},

   volume={25},

   number={3},

   pages={267–286},

   issn={0929-5593},

   year={2008},

   publisher={Springer}

}

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This paper presents a novel 3D-model-based computer-vision method for tracking the full six degree-of-freedom (dof) pose (position and orientation) of a rigid body, in real-time. The methodology has been targeted for autonomous navigation tasks, such as interception of or rendezvous with mobile targets. Tracking an object’s complete six-dof pose makes the proposed algorithm useful even when targets are not restricted to planar motion (e.g., flying or rough-terrain navigation). Tracking is achieved via a combination of textured model projection and optical flow. The main contribution of our work is the novel combination of optical flow with z-buffer depth information that is produced during model projection. This allows us to achieve six-dof tracking with a single camera. A localized illumination normalization filter also has been developed in order to improve robustness to shading. Real-time operation is achieved using GPU-based filters and a new data-reduction algorithm based on colour-gradient redundancy, which was developed within the framework of our project. Colour-gradient redundancy is an important property of colour images, namely, that the gradients of all colour channels are generally aligned. Exploiting this property provides a threefold increase in speed. A processing rate of approximately 80 to 100 fps has been obtained in our work when utilizing synthetic and real target-motion sequences. Sub-pixel accuracies were obtained in tests performed under different lighting conditions.
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