This work introduces a mobile robotic platform developed for autonomous navigation in wooded environments and for automatic estimation of the location of individual trees and their trunk diameters. The proposed system combines LiDAR measurements and camera images within a framework that leverages SLAM together with deep learning for trunks recognition. The proposed navigation and mapping approach is tested in a wooded area near Udine (Italy), using a skidsteered mobile robot. Experimental results demonstrate that the robot is capable of navigating effectively avoiding obstacles while generating forest maps enriched with tree trait information.
Autonomous Forest Navigation and Mapping: Field Validation of a Mobile Robotic System
Diego Tiozzo Fasiolo;Lorenzo Scalera;Eleonora Maset;Alessandro Gasparetto
2025-01-01
Abstract
This work introduces a mobile robotic platform developed for autonomous navigation in wooded environments and for automatic estimation of the location of individual trees and their trunk diameters. The proposed system combines LiDAR measurements and camera images within a framework that leverages SLAM together with deep learning for trunks recognition. The proposed navigation and mapping approach is tested in a wooded area near Udine (Italy), using a skidsteered mobile robot. Experimental results demonstrate that the robot is capable of navigating effectively avoiding obstacles while generating forest maps enriched with tree trait information.| File | Dimensione | Formato | |
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I-RIM_2025_extendedabstract.pdf
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