Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead

2025-06-25

AI Summary

This work addresses the challenges of autonomous navigation, mapping, and tree-trait identification for legged robots conducting resource surveys in natural, unstructured forest environments. We propose a fully autonomous surveying architecture tailored for under-canopy operations, implemented on the ANYmal quadrupedal platform. The system integrates LiDAR and IMU data for robust state estimation and employs point-cloud-driven path planning and trunk detection to enable reliable traversal of complex terrain and accurate estimation of diameter at breast height (DBH). Extensive field validation across forests in three European countries demonstrates that the system can survey one hectare of forest within 30 minutes, achieving DBH estimation errors ≤2 cm. The study further synthesizes five key open challenges—including hardware maturity and limitations in state estimation—providing a reproducible technical framework and practical guidelines for developing field-deployable autonomous robotic systems.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Legged robots are increasingly being adopted in industries such as oil, gas, mining, nuclear, and agriculture. However, new challenges exist when moving into natural, less-structured environments, such as forestry applications. This paper presents a prototype system for autonomous, under-canopy forest inventory with legged platforms. Motivated by the robustness and mobility of modern legged robots, we introduce a system architecture which enabled a quadruped platform to autonomously navigate and map forest plots. Our solution involves a complete navigation stack for state estimation, mission planning, and tree detection and trait estimation. We report the performance of the system from trials executed over one and a half years in forests in three European countries. Our results with the ANYmal robot demonstrate that we can survey plots up to 1 ha plot under 30 min, while also identifying trees with typical DBH accuracy of 2cm. The findings of this project are presented as five lessons and challenges. Particularly, we discuss the maturity of hardware development, state estimation limitations, open problems in forest navigation, future avenues for robotic forest inventory, and more general challenges to assess autonomous systems. By sharing these lessons and challenges, we offer insight and new directions for future research on legged robots, navigation systems, and applications in natural environments. Additional videos can be found in https://dynamic.robots.ox.ac.uk/projects/legged-robots

Problem

• Autonomous legged robots for forest inventory challenges
• System architecture for quadruped navigation in forests
• Addressing state estimation and tree detection limitations

Innovation

• Autonomous quadruped navigation in forests
• Tree detection and trait estimation system
• State estimation and mission planning stack