Camera calibration refers to the modeling of the relationship between the coordinates of object points and their projections on the image plane. This is usually done by parametric models that describe the physical properties of the lens systems and camera assemblies, such as the camera principal distance, the principal point, and various types of optical distortions. In photogrammetry, accurate knowledge of the parameters of such models, often referred to as Interior Orientation(IO), is of ultimate importance. In this work, we target advanced corridor mapping applications with UAVs. In this scenario, the camera calibration is not completely observable due to the unfavorable geometry of the flight trajectory (e.g., no cross-flight lines available and a single altitude) and needs to be determined beforehand. Further challenges are introduced by the limited mechanical stability of UAV-grade cameras. This may cause slight variations in the IO that need to be recovered while processing production flights. We review and compare two well-known camera models, the Brown-Conrady and the Ebner's self-calibration functions, in 36 calibration setups and provide a discussion of the results, where sub ground sampling distance accuracy in the checkpoints was achieved for some, but not all, configurations.