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Combining complementary edge, point and color cues in model-based tracking for highly dynamic scenes

Antoine Petit 1 Eric Marchand 1 Keyvan Kanani 2
1 Lagadic - Visual servoing in robotics, computer vision, and augmented reality
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This paper focuses on the issue of estimating the complete 3D pose of the camera with respect to a complex object, in a potentially highly dynamic scene, through model- based tracking. We propose to robustly combine complementary geometrical edge and point features with color based features in the minimization process. A Kalman filtering and pose pre- diction process is also suggested to handle potential large inter- frame motions. In order to deal with complex 3D models, our method takes advantage of hardware acceleration. Promising results, outperforming classical state-of-art approaches, have been obtained on various real and synthetic image sequences, with a focus on space robotics applications.
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Submitted on : Wednesday, February 19, 2014 - 11:43:15 AM
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Antoine Petit, Eric Marchand, Keyvan Kanani. Combining complementary edge, point and color cues in model-based tracking for highly dynamic scenes. IEEE Int. Conf. on Robotics and Automation, ICRA'14, Jun 2014, Hong-Kong, Hong Kong SAR China. ⟨hal-00949197⟩

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