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

• URL: http://arxiv.org/abs/2306.15517v1
• Date: Tue, 27 Jun 2023 14:46:09 GMT
• Title: Enhancing Navigation Benchmarking and Perception Data Generation for Row-based Crops in Simulation
• Authors: Mauro Martini, Andrea Eirale, Brenno Tuberga, Marco Ambrosio, Andrea Ostuni, Francesco Messina, Luigi Mazzara, Marcello Chiaberge
• Abstract summary: This paper presents a synthetic dataset to train semantic segmentation networks and a collection of virtual scenarios for a fast evaluation of navigation algorithms. 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.
• Score: 0.3518016233072556
• License: http://creativecommons.org/licenses/by/4.0/
• 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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