Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation
| Autor: | Martini, Mauro, Eirale, Andrea, Tuberga, Brenno, Ambrosio, Marco, Ostuni, Andrea, Messina, Francesco, Mazzara, Luigi, Chiaberge, Marcello |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and infield validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras. In this context, the contribution of this work is twofold: a synthetic dataset to train deep semantic segmentation networks together with a collection of virtual scenarios for a fast evaluation of navigation algorithms. Moreover, an automatic parametric approach is developed to explore different field geometries and features. The simulation framework and the dataset have been evaluated by training a deep segmentation network on different crops and benchmarking the resulting navigation. Comment: Accepted at the 14th European Conference on Precision Agriculture (ECPA) 2023 |
| Databáze: | arXiv |
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