Abstract
Robot grasping and human-robot handover (HRH) tasks can significantly facilitate people’s production and life. In these tasks, robots need to obtain the real-time 3D position of the object, and the distance from the object to the camera plane is the critical information to get the object position. Currently, depth camera-based distance detection methods always need additional equipment, which results in more complexity and cost. In contrast, RGB camera-based methods often assume that the object’s size is known or the object is at a fixed height. To make distance detection more adaptive and with low cost, a novel and efficient distance detection method based on monocular RGB images is proposed in this paper. With a simple marker, the method can estimate the object’s distance in real-time from the pixel information obtained by a general, lightweight target detector. Experiments on the Baxter robot platform show the effectiveness of the proposed method, where the success rate of the grasping test reaches 87.5%, and the success rate of the HRH test goes 84.7%.
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Acknowledgement
This work was supported in part by the Key Program of NSFC (Grant No. U1908214), Special Project of Central Government Guiding Local Science and Technology Development (Grant No. 2021JH6/10500140), Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province, Dalian and Dalian University, the Scientific Research fund of Liaoning Provincial Education Department (No. L2019606), Dalian University Scientific Research Platform Project (No. 202101YB03), and in part by the Science and Technology Innovation Fund of Dalian (Grant No. 2020JJ25CY001).
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Liu, D. et al. (2021). A Novel and Efficient Distance Detection Based on Monocular Images for Grasp and Handover. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-92635-9_37
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