Robust Dense Visual Odometry For RGB-D Cameras In A Dynamic Environment

Abstract : —The aim of our work is to estimate the camera motion from RGB-D images in a dynamic scene. Most of the existing methods have a poor localization performance in such environments, which makes them inapplicable in real world conditions. In this paper, we propose a new dense visual odometry method that uses RANSAC to cope with dynamic scenes. We show the efficiency and robustness of the proposed method on a large set of experiments in challenging situations and from publicly available benchmark dataset. Additionally, we compare our approach to another state-of-art method based on M-estimator that is used to deal with dynamic scenes. Our method gives similar results on benchmark sequences and better results on our own dataset.
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Communication dans un congrès
International Conference on Advanced Robotics ICAR 2015, Jul 2015, Istanbul, Turkey. 2015
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https://hal.inria.fr/hal-01212043
Contributeur : Dib Abdallah <>
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Abdallah Dib, François Charpillet. Robust Dense Visual Odometry For RGB-D Cameras In A Dynamic Environment. International Conference on Advanced Robotics ICAR 2015, Jul 2015, Istanbul, Turkey. 2015. 〈hal-01212043〉

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