Autonomous Navigation in Rows of Trees and High Crops with Deep Semantic Segmentation

Navone, Alessandro; Martini, Mauro; Ostuni, Andrea; Angarano, Simone; Chiaberge, Marcello

doi:10.1109/ecmr59166.2023.10256334

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2024

2025

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Cited by 4 publications

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Lavender Autonomous Navigation with Semantic Segmentation at the Edge

Navone,

Romanelli,

Ambrosio

et al. 2025

Communications in Computer and Information Science

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Lavender Autonomous Navigation with Semantic Segmentation at the Edge

Navone,

Romanelli,

Ambrosio

et al. 2025

Communications in Computer and Information Science

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Enhancing visual autonomous navigation in row-based crops with effective synthetic data generation

Martini,

Ambrosio,

Navone

et al. 2024

Precision Agric

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Introduction Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field 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. Materials and methods In this context, the contribution of this work resides in a complete framework to fully exploit synthetic data for a robust visual control of mobile robots. A wide realistic multi-crops dataset is accurately generated to train deep semantic segmentation networks and enabling robust performance in challenging real-world conditions. An automatic parametric approach enables an easy customization of virtual field geometry and features for a fast reliable evaluation of navigation algorithms. Results and conclusion The high quality of the generated synthetic dataset is demonstrated by an extensive experimentation with real crops images and benchmarking the resulting robot navigation both in virtual and real fields with relevant metrics.

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