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FLARE: Fast Autonomous Aerial Exploration in Large-Scale 3D Scenarios Using Actively Rotated LiDAR

Zhiwen Zhu,Yuhao Fang,X. Xiao,Ximin Lyu,Jie Mei,Boyu Zhou

Published 2025 in IEEE Transactions on Automation Science and Engineering

ABSTRACT

Autonomous aerial vehicles have emerged as critical platforms for 3D environmental mapping, yet existing LiDAR-based systems struggle to balance efficiency and compactness in large-scale scenarios. Conventional designs rigidly mount LiDAR with a narrow vertical field of view, necessitating inefficient vertical maneuvers for coverage. While rotating LiDARs can mitigate this limitation, they are often burdensome for lightweight aerial platforms and require processing more expansive 3D data streams. To address these challenges, we present FLARE, a co-designed aerial exploration system integrating a lightweight actively rotated LiDAR with a hierarchical planning framework. The micro-servo-actuated LiDAR dynamically adjusts its orientation via online planning, effectively expanding its sensing field without incurring substantial system complexity. Moreover, FLARE employs a hierarchical frontier clustering method that supports multilayer coarse-to-fine planning, balancing computational load and exploration performance to ensure efficient operation even in large-scale scenarios. Both simulation and fully onboard real-world experiments validate the system’s effectiveness, demonstrating complete coverage with shorter trajectories and reduced flight time compared to existing methods. Note to Practitioners—This work addresses the challenge of efficiently mapping large-scale 3D environments, such as industrial plants, mining sites, or historical structures, using autonomous drones. Existing systems often require excessive flight time to scan vertically extended spaces (e.g., tall chimneys, deep pits) due to fixed sensor orientations and limited vertical sensing range. These inefficiencies increase operational costs and delay critical tasks like structural inspections or hazard assessments. We propose a practical solution combining a mechanically simplified rotating LiDAR module with an adaptive path planning algorithm. Unlike conventional drones that must move laterally to scan vertical surfaces, our system actively adjusts the LiDAR’s scanning direction mid-flight. This reduces redundant movements in tall structures, significantly shortening mission time and energy consumption. The accompanying planning software hierarchically coordinates long-range route planning with real-time adjustments of drone motion and LiDAR orientation, ensuring thorough coverage without overwhelming computational resources.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    IEEE Transactions on Automation Science and Engineering

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/TASE.2025.3631098
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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