Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows
This work addresses the problem of high-cost sensors and computational workloads in precision agriculture for vineyard navigation, offering an affordable and reliable solution for autonomous robotic platforms.
The paper tackled autonomous navigation in vineyards by developing a control algorithm that uses a custom-trained segmentation network and a low-range RGB-D camera to produce smooth trajectories and stable control, demonstrating effectiveness and robustness through extensive real-world and simulated experiments.
Precision agriculture is a fast-growing field that aims at introducing affordable and effective automation into agricultural processes. Nowadays, algorithmic solutions for navigation in vineyards require expensive sensors and high computational workloads that preclude large-scale applicability of autonomous robotic platforms in real business case scenarios. From this perspective, our novel proposed control leverages the latest advancement in machine perception and edge AI techniques to achieve highly affordable and reliable navigation inside vineyard rows with low computational and power consumption. Indeed, using a custom-trained segmentation network and a low-range RGB-D camera, we are able to take advantage of the semantic information of the environment to produce smooth trajectories and stable control in different vineyards scenarios. Moreover, the segmentation maps generated by the control algorithm itself could be directly exploited as filters for a vegetative assessment of the crop status. Extensive experimentations and evaluations against real-world data and simulated environments demonstrated the effectiveness and intrinsic robustness of our methodology.