Automated Target-Free Network Orienation and Camera Calibration

Automated close-range photogrammetric network orientation and camera calibration has traditionally been associated with the use of coded targets in the object space to allow for an initial relative orientation (RO) and subsequent spatial resection of the images. However, over the last decade, advances coming mainly from the computer vision (CV) community have allowed for fully automated orientation via feature-based matching techniques. There are a number of advantages in such methodologies for various types of applications, as well as for cases where the use of artificial targets might be not possible or preferable, for example when attempting calibration from low-level aerial imagery, as with UAVs, or when calibrating long-focal length lenses where small image scales call for inconveniently large coded targets. While there are now a number of CV-based algorithms for multi-image orientation within narrow-baseline networks, with accompanying open-source software, from a photogrammetric standpoint the results are typically disappointing as the metric integrity of the resulting models is generally poor, or even unknown. The objective addressed in this paper is target-free automatic multi-image orientation, maintaining metric integrity, within networks that incorporate wide-baseline imagery. The focus is on both the development of a methodology that overcomes the shortcomings that can be present in current CV algorithms, and on the photogrammetric priorities and requirements that exist in current processing pipelines. This paper also reports on the application of the proposed methodology to automated target-free camera self-calibration and discusses the process via practical examples.