A Proof-of-Concept Free-Flight Photogrammetric Framework Based on Monocular Vision and Sensor-Group Displacement Fusion

As unmanned aerial vehicles (UAVs) have increasingly become aerial imaging platforms, the reliance of traditional photogrammetry on ground control points (GCPs) remains a major limitation in complex terrain, confined spaces, and scenarios where control points are difficult to deploy. To address this issue, this study proposes a proof-of-concept framework for free-flight photogrammetry based on the fusion of monocular vision and sensor-group displacement information. The framework employs a rigid point set station-displacement algorithm to compute the exterior orientation elements between adjacent measurement stations, providing a feasible approach for multi-station pose propagation under control-point-free conditions. In addition, a composite weighting strategy incorporating the effects of optical distortion and rigid-body consistency evaluation is developed to improve the rational use of point-set information during station-displacement computation. To evaluate the feasibility of the proposed method, numerical simulations were first conducted to analyze the variation patterns of exterior orientation computation and target-point reconstruction under different sampling intervals and error conditions. Subsequently, an indoor controlled bench-top experimental platform was constructed to physically validate the complete workflow of the proposed method. The bench-top experimental results show that the overall mean three-dimensional positioning error of the two cross-station image pairs was 15.450 mm, and the maximum three-dimensional positioning error was 36.685 mm. The mean absolute distance errors for station 1–station 2 and station 1–station 3 were 9.230 mm and 12.436 mm, respectively. These results indicate that the proposed method can complete station-displacement-based exterior orientation computation and three-dimensional target measurement in a controlled physical scenario, demonstrating clear proof-of-concept significance. It should be noted that UAV measurement experiments under real flight conditions have not yet been completed in this study, and further validation on an actual UAV platform is still required.