Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone

2025-01-21

AI Summary

In GNSS-denied dense forest environments, autonomous UAV navigation remains challenging, and inversion accuracy of individual tree parameters—particularly diameter at breast height (DBH)—is typically low. To address these issues, this study proposes a lightweight under-canopy autonomous UAV system. It integrates an open-source robotics framework with a miniature binocular photogrammetry system, incorporating visual-inertial odometry (VIO), real-time SLAM, stereo matching, 3D point cloud reconstruction, and a deep learning-based stem detection algorithm. The system enables fully autonomous obstacle-avoidance flight and high-precision retrieval of individual tree structural parameters. Field validation in boreal coniferous forests achieved a stem detection rate of 79.31%; the overall DBH estimation RMSE was 3.33 cm (12.79%), decreasing to 1.16 cm (5.74%) for small-diameter trees (<30 cm). These results significantly enhance automation capability for forest resource inventory in GNSS-denied under-canopy scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Multiagent Systems: Multiagent Systems under Uncertainty
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Drones are increasingly used in forestry to capture high-resolution remote sensing data. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. Inside dense forests, reliance on the Global Navigation Satellite System (GNSS) for localization is not feasible. Additionally, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open-source methods and validating its performance for data collection inside forests. The autonomous flight capability was evaluated through multiple test flights in two boreal forest test sites. The tree parameter estimation capability was studied by conducting diameter at breast height (DBH) estimation using onboard stereo camera data and photogrammetric methods. The prototype conducted flights in selected challenging forest environments, and the experiments showed excellent performance in forest reconstruction with a miniaturized stereoscopic photogrammetric system. The stem detection algorithm managed to identify 79.31 % of the stems. The DBH estimation had a root mean square error (RMSE) of 3.33 cm (12.79 %) and a bias of 1.01 cm (3.87 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 cm (5.74 %), and the bias was 0.13 cm (0.64 %). When considering the overall performance in terms of DBH accuracy, autonomy, and forest complexity, the proposed approach was superior compared to methods proposed in the scientific literature. Results provided valuable insights into autonomous forest reconstruction using drones, and several further development topics were proposed.

Problem

• Autonomous Drone Navigation
• Tree Diameter Estimation
• Forest Data Collection

Innovation

• Advanced Drone Prototype
• Stereo Camera System
• Autonomous Flight in Dense Forests