Abstract
Abstract. In photogrammetry a camera is considered calibrated if its interior orientation parameters are known. These encompass the principal distance, the principal point position and some Additional Parameters used to model possible systematic errors. The current state of the art for automated camera calibration relies on the use of coded targets to accurately determine the image correspondences. This paper presents a new methodology for the efficient and rigorous photogrammetric calibration of digital cameras which does not require any longer the use of targets. A set of images depicting a scene with a good texture are sufficient for the extraction of natural corresponding image points. These are automatically matched with feature-based approaches and robust estimation techniques. The successive photogrammetric bundle adjustment retrieves the unknown camera parameters and their theoretical accuracies. Examples, considerations and comparisons with real data and different case studies are illustrated to show the potentialities of the proposed methodology.
Highlights
Accurate camera calibration and image orientation procedures are a necessary prerequisite for the extraction of precise and reliable 3D metric information from images (Gruen and Huang, 2001)
The consistence and accuracy of the first targetless calibration experiement were verified using a special testfield composed of 21 circular targets which are used as Ground Control Points (GCPs)
This paper has presented a new procedure for camera calibration based on the natural texture of an object which has to be properly selected
Summary
Accurate camera calibration and image orientation procedures are a necessary prerequisite for the extraction of precise and reliable 3D metric information from images (Gruen and Huang, 2001). With the very rapid growth in adoption of off-the-shelf (or consumer-grade) digital cameras for 3D measurement applications, there are many situations where the geometry of the image network cannot support the robust recovery of camera interior parameters via on-the-job calibration For this reason, stand-alone and targetbased camera calibration has again emerged as an important issue in close-range photogrammetry. The algorithms are usually based on perspective or projective camera models, with the most popular approach being the well-known self-calibrating bundle adjustment (Brown, 1976; Fraser, 1997; Gruen and Beyer, 2001) It was first introduced in close-range photogrammetry in the early 1970s by Brown (1971). Part of this might well be explained in terms of a lack of emphasis on (and interest in) accuracy aspects and a basic premise that nothing whatever needs to be known about the camera which is to be calibrated within a linear projective rather than Euclidean scene reconstruction
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