This invention discloses an intelligent
analysis method for
bridge inspection using an unmanned aerial vehicle (UAV)
remote sensing platform. To address the problem of high-precision mapping and re-inspectionable localization of bridge defects from pixel coordinates, and to suppress localization drift under
occlusion and multipath conditions caused by the Global Navigation
Satellite System (GNSS), this invention collects and time-aligns image sequences, inertial measurement data, and GNSS observations. Based on
satellite geometry and
signal quality, it performs multipath evaluation, outputting reliability scores and observation biases, and corrects these biases. A
factor graph containing inertial, visual, and GNSS constraints is constructed, and a switch variable is introduced for the GNSS constraints. Prior values are set based on the reliability
score to adaptively adjust weights. Nonlinear optimization is performed to obtain the
pose sequence and
covariance. Furthermore, a
transformer matching method is used to generate closed-loop constraints. A joint decision is made combining matching confidence,
pose covariance, and reliability
score, and weighted additions are added to the
factor graph for update optimization. Defect detection is performed on the images to obtain defect pixel coordinates, and back-projection is used to intersect the bridge component model to obtain component identification and 3D position. A re-inspection localization range is generated based on
covariance propagation. This achieves the technical effect of high-precision localization of bridge defects and output of a re-inspectionable range.