Depth-Based Visual Servoing Using Low-Accurate Arm

Abstract : This paper proposes a visual-servoing method dedicated to grasping of daily-life objects. In order to obtain an affordable solution, we use a low-accurate robotic arm. Our method corrects errors by using an RGB-D sensor. It is based on SURF invariant features which allows us to perform object recognition at a high frame rate. We define regions of interest based on depth segmentation, and we use them to speed-up the recognition and to improve reliability. The system has been tested on a real-world scenario. In spite of the lack of accuracy of all the components and the uncontrolled environment, it grasps objects successfully on more than 95 percents of the trials.
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Communication dans un congrès
Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, Aug 2016, Sapporo, Japan. 2016
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https://hal.inria.fr/hal-01416248
Contributeur : Ludovic Hofer <>
Soumis le : mercredi 14 décembre 2016 - 11:22:13
Dernière modification le : jeudi 11 janvier 2018 - 06:20:17

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  • HAL Id : hal-01416248, version 1
  • ARXIV : 1612.03784

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Ludovic Hofer, Michio Tanaka, Hakaru Tamukoh, Amir Ali Forough Nassiraei, Takashi Morie. Depth-Based Visual Servoing Using Low-Accurate Arm. Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems, Aug 2016, Sapporo, Japan. 2016. 〈hal-01416248〉

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