Abstract
The ability of a robot to sense and “perceive" its surroundings to interact and influence various objects of interest by grasping them, using vision-based sensors is the main principle behind vision based Autonomous Robotic Grasping. To realise this task of autonomous object grasping, one of the critical sub-tasks is the 6D Pose Estimation of a known object of interest from sensory data in a given environment. The sensory data can include RGB images and data from depth sensors, but determining the object’s pose using only a single RGB image is cost-effective and highly desirable in many applications. In this work, we develop a series of convolutional neural network-based pose estimation models without post-refinement stages, designed to achieve high accuracy on relevant metrics for efficiently estimating the 6D pose of an object, using only a single RGB image. The designed models are incorporated into an end-to-end pose estimation pipeline based on Unity and ROS Noetic, where a UR3 Robotic Arm is deployed in a simulated pick-and-place task. The pose estimation performance of the different models is compared and analysed in both same-environment and cross-environment cases utilising synthetic RGB data collected from cluttered and simple simulation scenes constructed in Unity Environment. In addition, the developed models achieved high Average Distance (ADD) metric scores greater than 93% for most of the real-life objects tested in the LINEMOD dataset and can be integrated seamlessly with any robotic arm for estimating 6D pose from only RGB data, making our method effective, efficient and generic.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Stands for 6D Pose Single Stage Detector
- 2.
Stands for Deep Object Single Shot Estimator of 6D object pose
- 3.
Attention High Resolution Deep Object Single Shot Estimator of 6D object pose.
References
Calli, B., Walsman, A., Singh, A., Srinivasa, S., Abbeel, P., Dollar, A.M.: Benchmarking in manipulation research: Using the yale-cmu-berkeley object and model set. IEEE Robot. Automation Mag. 22(3), 36–52 (2015). https://doi.org/10.1109/MRA.2015.2448951
Chen, B., Parra, A., Cao, J., Li, N., Chin, T.J.: End-to-end learnable geometric vision by backpropagating pnp optimization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8100–8109 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., Navab, N.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 548–562. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_42
Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: Ssd-6d: Making rgb-based 3d detection and 6d pose estimation great again. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1521–1529 (2017)
Lepetit, V., Moreno-Noguer, F., Fua, P.: Epnp: an accurate o (n) solution to the pnp problem. Int. J. Comput. Vision 81(2), 155 (2009)
Nibali, A., He, Z., Morgan, S., Prendergast, L.: Numerical coordinate regression with convolutional neural networks. arXiv preprint arXiv:1801.07372 (2018)
Peng, S., Zhou, X., Liu, Y., Lin, H., Huang, Q., Bao, H.: Pvnet: pixel-wise voting network for 6dof object pose estimation. IEEE Trans. Pattern Anal. Mach. Intell. (2020)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Tejani, A., Tang, D., Kouskouridas, R., Kim, T.-K.: Latent-class hough forests for 3D object detection and pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 462–477. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_30
Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6d object pose prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 292–301 (2018)
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., Abbeel, P.: 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), pp. 23–30. IEEE (2017)
Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., Birchfield, S.: Deep object pose estimation for semantic robotic grasping of household objects. arXiv preprint arXiv:1809.10790 (2018)
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531–11539 (2020)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes. arXiv preprint arXiv:1711.00199 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Deevi, S.A., Mishra, D. (2023). Expeditious Object Pose Estimation for Autonomous Robotic Grasping. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-31417-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31416-2
Online ISBN: 978-3-031-31417-9
eBook Packages: Computer ScienceComputer Science (R0)