A seamless LiDAR/IMU/RTK fused localization method for UAV-Based bridge inspection

Abstract

The Simultaneous Localization and Mapping (SLAM) technology is fundamental to the autonomous navigation of Unmanned Aerial Vehicles (UAVs) and holds significant value for the realization of UAV-based bridge inspections. However, conventional SLAM methods for UAV face challenges related to low continuity and weak reliability across different scenes, making it difficult to meet the requirements for comprehensive bridge localization and mapping. To address the limitations of existing UAV-based SLAM approaches, we propose a seamless SLAM system that integrates IMU, LiDAR, and RTK. In open scenes (such as the top and sides of a bridge), high-precision absolute localization is achieved by fusing IMU and RTK through an iterative error-state Kalman filter (IESKF). In occluded environments (such as the underside of a bridge), an IMU/LiDAR odometry is used to recursively estimate the UAV’s pose. In cross-scene situations (when the UAV passes through a bridge arch), the quality of sensor data is evaluated based on an interactive multi-model (IMM), and an adaptive switching mechanism is employed between two localization modes—IMU/RTK mode and IMU/LiDAR mode—ensuring smooth and seamless multi-source fusion localization even in the presence of sensor signal fluctuations. To validate the effectiveness of our method, extensive tests were conducted on several real-world bridge scenarios. The results show that our method can achieve centimetre-level cross-scene localization accuracy in bridge inspection applications, which indicates its feasibility and effectiveness.