In this paper, we present the results of the development of an autonomous mobile robot for forest monitoring and mapping within the AI4FOREST project. This research project funded by the Italian National Recovery and Resilience Plan aims to design and implement an autonomous robotic system capable of navigating into a forest to create a digital twin of the environment and estimate the tree parameters. The proposed system is built on a Scout 2.0 wheeled mobile robot by AgileX that integrates a simultaneous localization and mapping approach for autonomous navigation and 3D reconstruction of the forest. Furthermore, the mobile robot is capable of detecting trees and estimating their diameters from point cloud data and a vision-based deep learning architecture. Experimental results in a wooded area demonstrate the capability of the robot for autonomous forest monitoring and mapping.
Mobile Robotics for Forest Monitoring and Mapping Within the AI4FOREST Project / Scalera, L., Tiozzo Fasiolo, D., Maset, E., Carabin, G., Seriani, S., De Lorenzo, A., Alberti, G., Gasparetto, A.. - 191:(2025), pp. 279-288. (Mechanical Engineering Solutions: Design, Simulation, Testing, Manufacturing (MES) Yerevan, Armenia Settembre 2025) [10.1007/978-3-032-02352-0_27].
Mobile Robotics for Forest Monitoring and Mapping Within the AI4FOREST Project
Seriani, Stefano;De Lorenzo, Andrea;
2025-01-01
Abstract
In this paper, we present the results of the development of an autonomous mobile robot for forest monitoring and mapping within the AI4FOREST project. This research project funded by the Italian National Recovery and Resilience Plan aims to design and implement an autonomous robotic system capable of navigating into a forest to create a digital twin of the environment and estimate the tree parameters. The proposed system is built on a Scout 2.0 wheeled mobile robot by AgileX that integrates a simultaneous localization and mapping approach for autonomous navigation and 3D reconstruction of the forest. Furthermore, the mobile robot is capable of detecting trees and estimating their diameters from point cloud data and a vision-based deep learning architecture. Experimental results in a wooded area demonstrate the capability of the robot for autonomous forest monitoring and mapping.| File | Dimensione | Formato | |
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