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.

翻译：服务机器人技术近期推动了精准农业的发展，通过高效自主导航解决方案实现了多种自动化流程。然而，数据生成与实地验证活动制约了大规模自主平台的进展。模拟环境与深度视觉感知正成为低成本RGB-D相机下加速鲁棒导航开发的成熟工具。在此背景下，本文的贡献体现在两个方面：一是用于训练深度语义分割网络的合成数据集，二是用于快速评估导航算法的虚拟场景集合。此外，本文还开发了一种自动参数化方法，用以探索不同的田间几何结构与特征。通过在不同作物上训练深度分割网络并对生成的导航结果进行基准测试，我们对模拟框架与数据集进行了评估。