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

• Published in: Precision agriculture Vol. 25; no. 6; pp. 2881 - 2902
• Main Authors: Martini, Mauro, Ambrosio, Marco, Navone, Alessandro, Tuberga, Brenno, Chiaberge, Marcello
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-12-2024; Springer Nature B.V
• Subjects: Agriculture; Atmospheric Sciences; Autonomous navigation; Biomedical and Life Sciences; Chemistry and Earth Sciences; Computer Science; Crops; Datasets; Image processing; Image quality; Image segmentation; Life Sciences; Physics; Precision farming; Remote Sensing/Photogrammetry; Robot control; Robotics; Robust control; Semantic segmentation; Soil Science & Conservation; Statistics for Engineering; Synthetic data; Virtual networks; Visual control; Visual fields; Visual perception; Visual perception driven algorithms; Service robotics; Autonomous navigation; Synthetic data; Deep semantic segmentation
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-024-10157-6

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.