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3D Reconstruction in Noisy Agricultural Environments: A Bayesian Optimization Perspective for View Planning

Published 29 Sep 2023 in cs.RO and cs.AI | (2310.00145v2)

Abstract: 3D reconstruction is a fundamental task in robotics that gained attention due to its major impact in a wide variety of practical settings, including agriculture, underwater, and urban environments. This task can be carried out via view planning (VP), which aims to optimally place a certain number of cameras in positions that maximize the visual information, improving the resulting 3D reconstruction. Nonetheless, in most real-world settings, existing environmental noise can significantly affect the performance of 3D reconstruction. To that end, this work advocates a novel geometric-based reconstruction quality function for VP, that accounts for the existing noise of the environment, without requiring its closed-form expression. With no analytic expression of the objective function, this work puts forth an adaptive Bayesian optimization algorithm for accurate 3D reconstruction in the presence of noise. Numerical tests on noisy agricultural environments showcase the merits of the proposed approach for 3D reconstruction with even a small number of available cameras.

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References (39)
  1. A. Bacharis, H. J. Nelson, and N. Papanikolopoulos, “View planning using discrete optimization for 3d reconstruction of row crops,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 9195–9201.
  2. H. J. Nelson, C. E. Smith, A. Bacharis, and N. P. Papanikolopoulos, “Robust plant localization and phenotyping in dense 3d point clouds for precision agriculture,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 9615–9621.
  3. C. Peng and V. Isler, “View selection with geometric uncertainty modeling,” arXiv preprint arXiv:1704.00085, 2017.
  4. P. Roy and V. Isler, “Active view planning for counting apples in orchards,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 6027–6032.
  5. E. Vidal, N. Palomeras, K. Istenič, N. Gracias, and M. Carreras, “Multisensor online 3d view planning for autonomous underwater exploration,” Journal of Field Robotics, vol. 37, no. 6, pp. 1123–1147, 2020.
  6. W. Jing, J. Polden, P. Y. Tao, W. Lin, and K. Shimada, “View planning for 3d shape reconstruction of buildings with unmanned aerial vehicles,” in 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV).   IEEE, 2016, pp. 1–6.
  7. N. Smith, N. Moehrle, M. Goesele, and W. Heidrich, “Aerial path planning for urban scene reconstruction: A continuous optimization method and benchmark,” ACM Trans. Graph., vol. 37, no. 6, dec 2018. [Online]. Available: https://doi.org/10.1145/3272127.3275010
  8. D. Zermas, V. Morellas, D. Mulla, and N. Papanikolopoulos, “Extracting phenotypic characteristics of corn crops through the use of reconstructed 3d models,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 8247–8254.
  9. K. A. Tarabanis, P. K. Allen, and R. Y. Tsai, “A survey of sensor planning in computer vision,” IEEE transactions on Robotics and Automation, vol. 11, no. 1, pp. 86–104, 1995.
  10. G. H. Tarbox and S. N. Gottschlich, “Planning for complete sensor coverage in inspection,” Computer vision and image understanding, vol. 61, no. 1, pp. 84–111, 1995.
  11. C. Peng and V. Isler, “Adaptive view planning for aerial 3d reconstruction,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 2981–2987.
  12. S. Pan, H. Hu, and H. Wei, “Scvp: Learning one-shot view planning via set covering for unknown object reconstruction,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 1463–1470, 2022.
  13. B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: A review of Bayesian optimization,” Proc. IEEE, vol. 104, no. 1, pp. 148–175, 2015.
  14. J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian optimization of machine learning algorithms,” Neural Information Processing Systems, vol. 25, 2012.
  15. K. Korovina, S. Xu, K. Kandasamy, W. Neiswanger, B. Poczos, J. Schneider, and E. Xing, “Chembo: Bayesian optimization of small organic molecules with synthesizable recommendations,” International Conference on Artificial Intelligence and Statistics, pp. 3393–3403, 2020.
  16. Z. Wang and S. Jegelka, “Max-value entropy search for efficient Bayesian optimization,” International Conference on Machine Learning, pp. 3627–3635, 2017.
  17. A. Cully, J. Clune, D. Tarapore, and J.-B. Mouret, “Robots that can adapt like animals,” Nature, vol. 521, no. 7553, pp. 503–507, 2015.
  18. R. Marchant and F. Ramos, “Bayesian optimisation for informative continuous path planning,” in International Conference on Robotics and Automation, 2014, pp. 6136–6143.
  19. J. I. Vasquez-Gomez, L. E. Sucar, and R. Murrieta-Cid, “View planning for 3d object reconstruction with a mobile manipulator robot,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2014, pp. 4227–4233.
  20. S. Pan, H. Hu, H. Wei, N. Dengler, T. Zaenker, and M. Bennewitz, “One-shot view planning for fast and complete unknown object reconstruction,” arXiv preprint arXiv:2304.00910, 2023.
  21. Q. Lu, K. D. Polyzos, B. Li, and G. B. Giannakis, “Surrogate modeling for bayesian optimization beyond a single gaussian process,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 11 283–11 296, 2023.
  22. K. D. Polyzos, Q. Lu, and G. B. Giannakis, “Bayesian optimization with ensemble learning models and adaptive expected improvement,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, 2023.
  23. D. Nguyen, S. Gupta, S. Rana, A. Shilton, and S. Venkatesh, “Bayesian optimization for categorical and category-specific continuous inputs,” AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 5256–5263, 2020.
  24. S. Gopakumar, S. Gupta, S. Rana, V. Nguyen, and S. Venkatesh, “Algorithmic assurance: An active approach to algorithmic testing using Bayesian optimisation,” Neural Information Processing Systems, pp. 5470–5478, 2018.
  25. K. D. Polyzos, Q. Lu, and G. B. Giannakis, “Weighted ensembles for active learning with adaptivity,” arXiv:2206.05009, 2022.
  26. W. R. Thompson, “On the likelihood that one unknown probability exceeds another in view of the evidence of two samples,” Biometrika, vol. 25, no. 3/4, pp. 285–294, 1933.
  27. D. R. Jones, M. Schonlau, and W. J. Welch, “Efficient global optimization of expensive black-box functions,” Journal of Global optimization, vol. 13, no. 4, pp. 455–492, 1998.
  28. N. Srinivas, A. Krause, S. Kakade, and M. Seeger, “Gaussian process optimization in the bandit setting: No regret and experimental design,” in International Conference on Machine Learning, 2010.
  29. P. I. Frazier, “A tutorial on Bayesian optimization,” arXiv preprint arXiv:1807.02811, 2018.
  30. M. Matl, “Pyrender,” https://github.com/mmatl/pyrender, 2019.
  31. H. J. Nelson and N. Papanikolopoulos, “Learning continuous object representations from point cloud data,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 2446–2451.
  32. J. L. Schönberger and J.-M. Frahm, “Structure-from-motion revisited,” in Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
  33. J. L. Schönberger, E. Zheng, M. Pollefeys, and J.-M. Frahm, “Pixelwise view selection for unstructured multi-view stereo,” in European Conference on Computer Vision (ECCV), 2016.
  34. J. R. Gardner, G. Pleiss, D. Bindel, K. Q. Weinberger, and A. G. Wilson, “Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration,” in Advances in Neural Information Processing Systems, 2018.
  35. M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy, “BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization,” in Advances in Neural Information Processing Systems 33, 2020. [Online]. Available: http://arxiv.org/abs/1910.06403
  36. S. Kirkpatrick, C. D. Gelatt Jr, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, no. 4598, pp. 671–680, 1983.
  37. F. Williams, “Point cloud utils,” 2022, https://www.github.com/fwilliams/point-cloud-utils.
  38. Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep learning for 3d point clouds: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 12, pp. 4338–4364, 2020.
  39. T. Wu, L. Pan, J. Zhang, T. Wang, Z. Liu, and D. Lin, “Balanced chamfer distance as a comprehensive metric for point cloud completion,” Advances in Neural Information Processing Systems, vol. 34, pp. 29 088–29 100, 2021.
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