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arXiv · 2306.15517

Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation

Mauro Martini·Andrea Eirale·Brenno Tuberga·Marco Ambrosio·Andrea Ostuni·Francesco Messina·Luigi Mazzara·Marcello Chiaberge
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Abstract

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.

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Mauro Martini, Andrea Eirale, Brenno Tuberga, Marco Ambrosio, Andrea Ostuni, Francesco Messina, Luigi Mazzara, Marcello Chiaberge. 2023-06-27. Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation. https://arxiv.org/abs/2306.15517

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