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Active perception for fruit mapping and harvesting is a difficult task since occlusions occur frequently and the location as well as size of fruits change over time.
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S. Marangoz, T. Zaenker, R. Menon, and M. Bennewitz, “Fruit mapping with shape completion for autonomous crop monitoring,” in Proc. of the IEEE Intl. Conf. on Automation Science and Engineering (CASE) . IEEE, 2022
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M. Grinvald, F. Furrer, T. Novkovic, J. J. Chung, C. Cadena, R. Siegwart, and J. Nieto, “Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 3037–3044, July 2019
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Moveit.ros.org, urlhttps://github.com/ros-planning/moveit2/
Cited in the paper.
L. Gong, W. Wang, T. Wang, and C. Liu, “Robotic harvesting of the occluded fruits with a precise shape and position reconstruction approach,” Journal of Field Robotics (JFR) , vol. 39, no. 1, 2022
2022
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F. Magistri, E. Marks, S. Nagulavancha, I. Vizzo, T. Läbe, J. Behley, M. Halstead, C. McCool, and C. Stachniss, “Contrastive 3d shape completion and reconstruction for agricultural robots using rgb-d frames,” IEEE Robotics and Automation Letters (RA-L) , 2022
2022
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L. Schmid, M. N. Cheema, V. Reijgwart, R. Siegwart, F. Tombari, and C. Cadena, “Incremental 3d scene completion for safe and efficient exploration mapping and planning,” in ArXiv Preprint , 2022
2022
Closest in time.