GPS-free autonomous navigation in cluttered tree rows with deep semantic segmentation

Abstract

Segmentation-based autonomous navigation has recently been presented as an appealing approach to guiding robotic platforms through crop rows without requiring perfect GPS localization. Nevertheless, current techniques are restricted to situations where the distinct separation between the plants and the sky allows for the identification of the row’s center. However, tall, dense vegetation, such as high tree rows and orchards, is the primary cause of GPS signal blockage. In this study, we increase the overall robustness and adaptability of the control algorithm by extending the segmentation-based robotic guiding to those cases where canopies and branches occlude the sky and prevent the utilization of GPS and earlier approaches. An efficient Deep Neural Network architecture has been used to address semantic segmentation, performing the training with synthetic data only. Numerous vineyards and tree fields have undergone extensive testing in both simulation and real world to show the solution’s competitive benefits. The system achieved unseen results in orchards, with a Mean Average Error smaller than 9% of the maximum width of each row, improving state-of-the-art algorithms by disclosing new scenarios such as close canopy crops. The official code can be found at: https://github.com/PIC4SeR/SegMinNavigation.git . • We propose two variants of a novel segmentation-based approach for autonomous navigation in orchards and vineyards. • Our guidance algorithms are tested on previously unseen plant row scenarios, including vineyards, apple orchards, pergola vineyards, and arched hedges of trees. • An efficient segmentation neural network is trained using a synthetic multi-crop dataset, ensuring generalization from simulation to the real world. • The new methods are compared with state-of-the-art solutions in both simulated and real vineyards, demonstrating enhanced robustness and overall performance.