UAV navigation in large-scale GPS-denied bridge environments using fiducial marker-corrected stereo visual-inertial localisation

The use of Unmanned Aerial Vehicles (UAVs) for bridge inspection has gained popularity recently; however, accurately localising the UAV in GPS-denied areas is still challenging, which hinders the development of fully autonomous UAV-assisted bridge inspection solutions. This paper proposes a fiducial marker-corrected stereo visual-inertial localisation (FMC-SVIL) method, running on a resource-constrained onboard computer, to estimate UAV’s global pose underneath bridge girders. The proposed FMC-SVIL utilises an optimised stereo visual-inertial odometry for continuous relative pose estimation between consecutive camera frames and an improved AprilTag2-based measurement algorithm for accurate global referencing and periodic pose corrections. The method is validated through extensive experiments, and the results show that the FMC-SVIL achieved UAV localisation with a root mean square error of 0.416 m in sunny conditions and 0.340 m in cloudy conditions. FMC-SVIL outperforms the leading vision-based simultaneous localisation and mapping (SLAM) algorithms for flights over multiple bridge spans.