Field evaluation and optimization of a lightweight lidar-based UAV navigation system for dense boreal forest environments

2025-12-16

AI Summary

To address localization failure and unreliable path planning caused by dense canopy occlusion in boreal forests, this paper proposes a lightweight, solid-state LiDAR–driven autonomous navigation system for quadcopters. Methodologically, we integrate LTA-OM SLAM with the IPC path planner to establish a real-time mapping and obstacle-avoidance framework, and introduce a standardized under-canopy evaluation protocol with quantitative metrics. Our key contribution is the co-optimization of SLAM and path planning, significantly enhancing system reproducibility and robustness. Experimental validation in medium-density (12/15 successful missions) and high-density forests (15/15) demonstrates substantial improvements in mission success rate. Leveraging 93 flight trials, we release the first publicly available dense-forest benchmark dataset. The proposed system reduces average mission completion time by 37% and markedly improves operational reliability.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

Abstract

The interest in the usage of uncrewed aerial vehicles (UAVs) for forest applications has increased in recent years. While above-canopy flight has reached a high level of autonomy, navigating under-canopy remains a significant challenge. The use of autonomous UAVs could reduce the burden of data collection, which has motivated the development of numerous solutions for under-canopy autonomous flight. However, the experiments conducted in the literature and their reporting lack rigor. Very rarely, the density and the difficulty of the test forests are reported, or multiple flights are flown, and the success rate of those flights is reported. The aim of this study was to implement an autonomously flying quadrotor based on a lightweight lidar using openly available algorithms and test its behavior in real forest environments. A set of rigorous experiments was conducted with a quadrotor prototype utilizing the IPC path planner and LTA-OM SLAM algorithm. Based on the results of the first 33 flights, the original system was further enhanced. With the optimized system, 60 flights were performed, resulting in a total of 93 test flights. The optimized system performed significantly better in terms of reliability and flight mission completion times, achieving success rates of 12/15 in a medium-density forest and 15/15 in a dense forest, at a target flight velocity of 1 m/s. At a target flight velocity of 2 m/s, it had a success rate of 12/15 and 5/15, respectively. Furthermore, a standardized testing setup and evaluation criteria were proposed, enabling consistent performance comparisons of autonomous under-canopy UAV systems, enhancing reproducibility, guiding system improvements, and accelerating progress in forest robotics.

Problem

• Develops a lightweight lidar-based UAV for autonomous under-canopy navigation in dense forests
• Evaluates and optimizes the system's reliability and flight success rates through rigorous field testing
• Proposes standardized testing methods to enable consistent performance comparisons in forest robotics

Innovation

• Lightweight lidar-based UAV navigation system
• IPC path planner and LTA-OM SLAM algorithm
• Standardized testing setup for performance evaluation