# GPS-free Autonomous Navigation in Cluttered Tree Rows with Deep Semantic Segmentation Source: [GPS-free Autonomous Navigation in Cluttered Tree Rows with Deep Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:35:49.064Z Authors: Alessandro Navone, Mauro Martini, Marco Ambrosio, Andrea Ostuni, Simone Angarano, Marcello Chiaberge Published: 2024-04-08T09:26:31.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()