This study addresses the challenge of autonomous aerial vehicle positioning in indoor environments where GPS signals are unavailable, proposing a navigation approach that integrates Light Detection and Ranging (LiDAR) and Ultra-Wideband (UWB) technologies, specifically designed for railway tunnel applications. Initially, LiDAR is used to scan the tunnel and create a detailed three-dimensional map, while strategically placed UWB nodes provide precise distance information. During the map construction phase, an enhanced Gaussian Mixture Model (GMM) accurately captures environmental features, aided by an Expectation Maximization (EM) algorithm that constructs a two-dimensional grid map. In the positioning phase, the study utilizes odometry constraints, LiDAR point cloud matching constraints, and UWB distance measurement constraints to optimize global pose estimation. A fusion algorithm based on Bayesian estimation effectively integrates LiDAR and UWB data, achieving real-time and high-precision positioning for autonomous aerial vehicles. Experimental results demonstrate that the proposed scheme achieves sub-meter-level positioning accuracy in GPS-denied environments within railway tunnels, ensuring robust and reliable positioning capabilities for indoor autonomous aerial vehicles. This research presents a promising solution to enhance the efficiency and safety of autonomous operations in railway tunnel environments.