Experimental Research on Multi-Source Information Fusion-Based UAV Integrated Navigation in Bridge Areas

© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

Aiming at the inherent limitations of traditional bridge inspection methods, such as low efficiency, high operational risk, and limited inspection coverage, this study proposes an autonomous navigation and obstacle avoidance scheme for bridge inspection uncrewed aerial vehicles (UAVs) based on multi-source data fusion. Firstly, high-precision time synchronization is implemented. The time-stamped data is divided into three major data categories, namely visual images, LiDAR, and GNSS/IMU data. An improved Kalman filtering algorithm is then adopted to achieve spatiotemporal registration and error compensation of the multi-source data, which significantly enhances the accuracy and stability of environmental perception during UAV flight. Secondly, a hierarchical autonomous navigation strategy is designed by combining the structural characteristics of bridges. The strategy realizes global path planning based on bridge structural features and conducts real-time optimization of local obstacle avoidance paths, ensuring that UAVs maintain safe and efficient operation in complex bridge environments (e.g., narrow spaces, complex structural components, and variable weather conditions). Finally, experimental verification is conducted in a real bridge inspection scenario, and the results demonstrate that the proposed scheme outperforms traditional methods in key performance indicators, including navigation positioning accuracy, obstacle avoidance response speed, and inspection coverage rate. Specifically, the positioning error is reduced by 32%, the obstacle avoidance response time is reduced by 28%, and the inspection coverage rate is increased to over 96%. This research provides important technical support for the engineering application of UAVs in bridge inspection and holds practical significance for promoting the intelligent operation and maintenance of infrastructure. The proposed multi-source data fusion method and hierarchical navigation strategy can also serve as a reference for other UAV-based inspection tasks in complex industrial environments.

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