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Volume: 31 | Article ID: art00004
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Automatic Estimation of the Position and Orientation of the Drill to Be Grasped and Manipulated by the Disaster Response Robot Based on Analyzing Depth Camera Information
  DOI :  10.2352/ISSN.2470-1173.2019.7.IRIACV-452  Published OnlineJanuary 2019
Abstract

Towards the actualization of a disaster response robot that can locate and manipulate a drill at an arbitrary position with an arbitrary posture in disaster sites, this paper proposes a method that can estimate the position and orientation of the drill that is to be grasped and manipulated by the robot arm, by utilizing the depth camera information acquired by the depth camera. In this paper’s algorithm, first, using a conventional method, the target drill is detected on the basis of an RGB image captured by the depth camera, and 3D point cloud data representing the target is generated by combining the detection results and the depth image. Second, using our proposed method, the generated point cloud data is processed to estimate the information on the proper position and orientation for grasping the drill. More specifically, a pass through filter is applied to the generated 3D point cloud data obtained by the first step. Then, the point cloud is divided, and features are classified so that the chuck and handle are identified. By computing the centroid of the point cloud for the chuck, the position for grasping is obtained. By applying Principal Component Analysis, the orientation for grasping is obtained. Experiments were conducted on a simulator. The results show that our method could accurately estimate the proper configuration for the autonomous grasping a normal-type drill.

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Keishi Nishikawa, Jun Ohya, Hiroyuki Ogata, Kenji Hashimoto, Takashi Matsuzawa, Asaki Imai, Shunsuke Kimura, Atsuo Takanishi, "Automatic Estimation of the Position and Orientation of the Drill to Be Grasped and Manipulated by the Disaster Response Robot Based on Analyzing Depth Camera Informationin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2019,  pp 452-1 - 452-7,  https://doi.org/10.2352/ISSN.2470-1173.2019.7.IRIACV-452

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