Camera self-calibration determines the precision of UAV (Unmanned Aerial Vehicle) image AT (Aerial Triangulation). The UAV images collected from long transmission line corridors are critical configurations, which may lead to the "bowl effect" with camera self-calibration. To solve such problems, traditional methods rely on more than three GCPs (Ground Control Points), while this study designs a new self-calibration method with only one GCP. Methods
First, two categories camera distortion models, i.e., physical and mathematical model, are studies in details. Second, within an incremental SfM (Structure from Motion) framework, a camera self-calibration method is designed, which combines the strategies for initializing camera distortion parameters and fusing high-precision GNSS (Global Navigation Satellite System) observations. Results
The proposed algorithm is verified by using four UA datasets collected from two sites based on two data acquisition modes. The experimental results show that the proposed method can dramatically alleviate the "bowl effect" and improve the accuracy of AT, and the horizontal and vertical accuracy reach 0.06 m, respectively, when using one GCP. Conclusions
Compared with open-source and commercial software, the proposed method achieves competitive or better performance.