Deep Learning Based 6D Pose Estimation for Bin-Picking Using 3D Point Clouds
Authors: Hesheng Wang, Haoyu Wang, Chungang Zhuang
Estimating the 6D pose of objects is a core step for robot bin-picking tasks. The problem is that various objects are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a method to regress 6D poses by predicting three points for each object in the 3D point cloud through deep learning. To solve the ambiguity of symmetric pose, we propose a labeling method to help the network converge better. Based on the predicted pose, an iterative method is employed for pose optimization. In real-world experiments, our method outperforms the classical approach in both precision and recall.
Keywords: Pose estimation, deep learning, point cloud, bin-picking, 3D computer vision.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1512
 B. Drost, M. Ulrich, N. Navab, and S. Ilic, “Model globally, match locally: Efficient and robust 3D object recognition,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 998–1005.
 P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239–256, 1992.
 C. Papazov and D. Burschka, “An Efficient RANSAC for 3D Object Recognition in Noisy and Occluded Scenes,” in Computer Vision-ACCV 2010, PT I, Heidelberger Platz 3, D-14197 Berlin, Germany, 2011, vol. 6492, no. I, pp. 135–148.
 W. Abbeloos and T. Goedemé, “Point Pair Feature Based Object Detection for Random Bin Picking,” in 2016 13th Conference on Computer and Robot Vision (CRV), 2016, pp. 432–439.
 Vidal, C. Lin, and R. Martí, “6D pose estimation using an improved method based on point pair features,” in 2018 4th International Conference on Control, Automation and Robotics (ICCAR), 2018, pp. 405–409.
 Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox, “PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes.” 2018.
 C. Wang et al., “DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3338–3347.
 Y. He, W. Sun, H. Huang, J. Liu, H. Fan, and J. Sun, “PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11629–11638.
 Z. Dong et al., “PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking Scenarios,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019, pp. 1773–1780.
 K. Kleeberger, C. Landgraf, and M. F. Huber, “Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019, pp. 2573–2578.
 Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 23–30.
 C. R. Qi, O. Litany, K. He, and L. Guibas, “Deep Hough Voting for 3D Object Detection in Point Clouds,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 9276–9285.
 L. Jiang, H. Zhao, S. Shi, S. Liu, C. -W. Fu, and J. Jia, “PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 4866–4875.
 O. Cicek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. 19th International Conference. Proceedings: LNCS 9901, 2016, pp. 424–32.
 K. K. Liang, “Efficient conversion from rotating matrix to rotation axis and angle by extending Rodrigues’ formula.” 2018.