Abstract
This paper introduces a six degree-of-freedom (6DoF) pose estimation method for manipulating robots to construct a robust machine-vision system. Generally, 2DoF results obtained by traditional object detectors cannot meet the requirements of manipulating operations, where the posture of targets are additionally needed. Meanwhile, due to the sensitivity to light and the limitation to distance, the depth sensor of RGB-D cameras could not always be reliable. To overcome these problems, we study 6DoF pose estimation from a single RGB image. To reduce the complexity and computation, we divide the task into four stages, i.e., data collection and pre-processing, instance segmentation, keypoints prediction, and 2D-to-3D projection. We build the model with deep neural networks, and test it in practical manipulating tasks. The experimental results demonstrate the high accuracy and practicality of our method.
This research was funded by China Postdoctoral Science Foundation under grant 2020M672529, and China Southern Power Grid Science and Technology Project under grant GDKJXM20192276, GDKJXM20184840 and NYJS2020KJ005-12.
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Du, S., Zhang, X., Li, Z., Yue, J., Zou, Q. (2021). Object 6DoF Pose Estimation for Power Grid Manipulating Robots. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_5
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