The FriûlBot dataset: Experimental validation of an autonomous ground robot for vineyard 3D mapping

In this paper, we present the FriûlBot open-source dataset, collected by an autonomous mobile robot for vineyard 3D mapping and monitoring. The dataset is designed to provide a reference testbed for the development and benchmarking of localization, mapping, and multi-sensor data fusion algorithms. It includes detailed information on the robot status, point clouds of the vineyard, and multispectral images, offering valuable resources for future research on autonomous robotic systems in agriculture. The mobile robot employed for the dataset acquisition is capable of autonomously navigating, reaching GNSS way points, and building a map of the canopy integrating geometric and multispectral data. The navigation and mapping approach proposed in this work is based on a SLAM algorithm that integrates multiple odometry sources, and is designed for agricultural environments with plants arranged in rows, e.g., vineyards and orchards. The performance of the proposed mobile robot and navigation approach are tested during an extensive experimental campaign in a vineyard of University of Udine (Italy). During the tests, the robot successfully navigates along several vineyard rows, building point clouds of the environment that are merged with data regarding multiple vegetation indexes. The experimental results confirm the reliability of the autonomous mobile robot and the potential of the proposed dataset to foster further advances in robotics for vineyard 3D mapping.