ALM-LED: autonomous LiDAR mapping in underground space with Luojia explorer anti-collision drone

Abstract

With the development of urbanization, underground spaces have become an important part of human life. Accurately surveying and describing the spatial information of underground spaces is of significant importance. However, the complex environment of underground spaces, often characterized by darkness, narrowness, lack of structure, and GNSS-denied conditions, presents tremendous challenges for intelligent information acquisition and analysis in such environments. To address these challenges, we propose ALM-LED, an autonomous LiDAR mapping framework designed for underground environments, which integrates a cost-effective, Luojia explorer anti-collision drone system featuring a lightweight LiDAR sensor and a carbon fiber frame. This framework consists of two main modules: localization and mapping, and planning and control. Localization and mapping integrates LiDAR point cloud data, IMU data, as well as flight control barometer and magnetometer sensor data, enabling robust localization and high-precision mapping in GNSS-denied underground environments. Planning and control constructs a triple constrained cost function for flight trajectory optimization based on smoothness, dynamic feasibility, and collision penalty terms, providing autonomous flight paths for the anti-collision drone system and combining MAVROS, achieving robust control. To validate the proposed system and methods, we conducted experiments in one simulation scenario and two real-world underground scenarios. The experiments demonstrate that ALM-LED achieves average mapping efficiencies exceeding 100 m³/s in simulated environments and 50 m³/s in real-world scenarios when applied to underground spaces. The flight trajectory estimated by the localization and mapping subsystem is nearly identical to the target trajectory. Point cloud maps with volumes of 879 m³, 26313 m³, 22240m³ and 115m³ were generated in four real-world scenarios, with point cloud map accuracies reaching 0.034m, 0.31m, 0.088m and 0.053m, respectively. The experimental results indicate that ALM-LED can achieve efficient and accurate information acquisition in underground spaces, demonstrating high application potential. To support the research community, the key source code for this work is publicly available at the following repository: https://github.com/DCSI2022/ALM-LED.