LiDAR-Based Multi-Modal UAV Navigation Dataset for Robust Benchmarking in Complex-Structured, GNSS-Denied Industrial Environments

To address the problem of lacking effective evaluation benchmarks for UAV navigation algorithms in complex-structured, GNSS-denied industrial environments (e.g., fully enclosed stockyards), this paper proposes and open-sources a multi-modal UAV navigation dataset. The dataset is collected in a real steel plant enclosed stockyard, integrating LiDAR point clouds, IMU, RGB images, and high-precision total station ground truth trajectories, and specially designs ArUco markers to aid visual localization. Different from existing datasets targeting urban or campus scenes, this dataset realistically reflects the challenges of GNSS-denied signal, weak texture, high dust, and complex spatial grid structures in industrial environments. Through the evaluation of various mainstream LiDAR odometry and fusion navigation algorithms, the difficulties encountered by existing methods in this scenario are highlighted, and the potential of the proposed dataset as a valuable benchmark for developing and quantitatively testing highly robust navigation algorithms is suggested.