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6D Object Pose Estimation Using Keypoints and Part Affinity Fields

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RoboCup 2021: Robot World Cup XXIV (RoboCup 2021)

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Abstract

The task of 6D object pose estimation from RGB images is an important requirement for autonomous service robots to be able to interact with the real world. In this work, we present a two-step pipeline for estimating the 6 DoF translation and orientation of known objects. Keypoints and Part Affinity Fields (PAFs) are predicted from the input image adopting the OpenPose CNN architecture from human pose estimation. Object poses are then calculated from 2D-3D correspondences between detected and model keypoints via the PnP-RANSAC algorithm. The proposed approach is evaluated on the YCB-Video dataset and achieves accuracy on par with recent methods from the literature. Using PAFs to assemble detected keypoints into object instances proves advantageous over only using heatmaps. Models trained to predict keypoints of a single object class perform significantly better than models trained for several classes.

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Notes

  1. 1.

    The PnP algorithm requires at least four correspondences for a unique solution.

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Acknowledgments

This work was funded by grant BE 2556/18-2 of the German Research Foundation (DFG).

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Correspondence to Simon Bultmann .

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Zappel, M., Bultmann, S., Behnke, S. (2022). 6D Object Pose Estimation Using Keypoints and Part Affinity Fields. In: Alami, R., Biswas, J., Cakmak, M., Obst, O. (eds) RoboCup 2021: Robot World Cup XXIV. RoboCup 2021. Lecture Notes in Computer Science(), vol 13132. Springer, Cham. https://doi.org/10.1007/978-3-030-98682-7_7

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  • DOI: https://doi.org/10.1007/978-3-030-98682-7_7

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