A Sub-Scene-Based GNSS-Constrained Structure from Motion for Robust Long-Corridor UAV Image Reconstruction

In long-corridor Unmanned Aerial Vehicle (UAV) photogrammetry, weak imaging geometry can compromise camera parameter estimation and lead to systematic reconstruction deformation, commonly referred to as the bowl effect, in conventional structure-from-motion (SfM) pipelines. To address this problem, this paper proposes a sub-scene-based GNSS (Global Navigation Satellite System) constrained SfM framework for robust long-corridor UAV photogrammetric reconstruction. First, camera parameter gradients derived from epipolar geometry are used to construct a gradient-consistency cost for identifying stable sub-scenes. Second, GNSS-constrained incremental structureless bundle adjustment (BA) is performed to recover reliable initial camera poses and absolute scale based on image triplets and GNSS/POS observations. Finally, GNSS-weighted BA and inequality-constrained GNSS fusion are introduced to refine camera parameters under a single ground control point (GCP). Experiments on four UAV corridor datasets demonstrate that the proposed method effectively suppresses the bowl effect and stabilizes camera parameter estimation. The proposed method achieves average planar, vertical, and three-dimensional accuracies of 0.040 m, 0.032 m, and 0.051 m, respectively, using only one control point. Compared with the standard Colmap pipeline, the runtime is reduced by approximately 52%. In addition, the proposed method achieves a lower average three-dimensional checkpoint RMSE (0.051 m) than MicMac (0.056 m), Pix4D (0.074 m), Agisoft Metashape (0.078 m) and ContextCapture (0.060 m).