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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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Conference papers
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https://hal.inria.fr/hal-01416248
Contributor : Ludovic Hofer <>
Submitted on : Wednesday, December 14, 2016 - 11:22:13 AM
Last modification on : Monday, November 30, 2020 - 9:30:04 AM

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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. ⟨hal-01416248⟩

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