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Research Article

Deep Learning Based Object Attitude Estimation for a Laser Beam Control Research Testbed

ORCID Icon, , &
Article: 2151191 | Received 19 Aug 2022, Accepted 18 Nov 2022, Published online: 12 Dec 2022

References

  • Cardoza, I., J. P. García-Vázquez, A. Díaz-Ramírez, and V. Quintero-Rosas. 2022. Convolutional neural networks hyperparameter tunning for classifying firearms on images. Applied Artificial Intelligence 36:1–39. doi:10.1080/08839514.2022.2058165.
  • Chen, H., P. Wang, F. Wang, W. Tian, L. Xiong, and H. Li. 2022. Epro-PnP: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA (pp. 2781–90).
  • Fu, D., W. Li, S. Han, X. Zhang, Z. Zhan, and M. Yang. 2019. The aircraft pose estimation based on a convolutional neural network. Mathematical Problems in Engineering.
  • Grewal, M. S., and A. P. Andrews. 2014. John Wiley & Sons, Kalman filtering: Theory and Practie with MATLAB.
  • He, K., G. Gkioxari, P. Dollár, and R. Girshick. 2017. Mask R-CNN. In Proceedings of the IEEE international conference on computer vision, Venice, Italy (pp. 2961–69).
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, USA (pp. 770–78).
  • Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. 82:35–45. doi:10.1115/1.3662552.
  • Kim, P. 2011. Kalman filter for beginners: With MATLAB examples. CreateSpace.
  • Kim, P. 2013. Rigid body dynamics for beginners: Euler angles & quaternions.
  • Liao, L., H. Li, W. Shang, and L. Ma. 2022. An empirical study of the impact of hyper-parameter tuning and model optimization on the performance properties of deep neural networks. ACM Transactions on Software Engineering and Methodology (TOSEM) 31 (3):1–40. doi:10.1145/3506695.
  • Mahendran, S., M. Y. Lu, H. Ali, and R. Vidal. 2018. Monocular object orientation estimation using riemannian regression and classification networks. arXiv preprint arXiv:180707226.
  • MathWorks. n.d. What is a Convolutional Neural Network? Available at https://www.mathworks.com/discovery/convolutional-neural-network-matlab.html.
  • McGee, L. A., S. F. Schmidt, L. A. Mcgee, and S. F. Sc. 1985. Discovery of the kalman filter as a practical tool for aerospace and. Industry,” National Aeronautics and Space Administration, Ames Research.
  • Newell, A., K. Yang, and J. Deng. 2016. Stacked hourglass networks for human pose estima- tion. In European conference on computer vision, Amsterdam, Netherlands (pp. 483–99).
  • Pavlakos, G., X. Zhou, A. Chan, K. G. Derpanis, and K. Daniilidis. 2017. 6-dof object pose from semantic keypoints. In 2017 IEEE international conference on robotics and automation (ICRA), Marina Bay Sands, Singapore (pp. 2011–18).
  • Phisannupawong, T., P. Kamsing, P. Tortceka, and S. Yooyen. 2020. Vision-based attitude estimation for spacecraft docking operation through deep learning algorithm. In 2020 22nd International Conference on Advanced Communication Technology (ICACT), Pyeongchang, South Korea (pp. 280–84).
  • Proenc¸a, P. F., and Y. Gao. 2020. Deep learning for spacecraft pose estimation from pho- torealistic rendering 2020 IEEE International Conference on Robotics and Automation (ICRA), Virtual (pp. 6007–13).
  • Sharma, S., C. Beierle, and S. D’Amico. 2018. Pose estimation for non-cooperative spacecraft rendezvous using convolutional neural networks. In 2018 IEEE Aerospace Conference, Yellowstone, USA (pp. 1–12).
  • Simon, D. 2006. Optimal state estimation: Kalman, H, and nonlinear approaches. John Wiley & Sons.
  • Sun, K., B. Xiao, D. Liu, and J. Wang. 2019. Deep high-resolution representation learning for human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, USA (pp. 5693–703).