  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.
