Bundle Adjustment Methods in Engineering Photogrammetry

Granshaw, Stuart I.

doi:10.1111/j.1477-9730.1980.tb00020.x

Cited by 189 publications

(105 citation statements)

References 9 publications

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“…The rank deficiency of the Least Squares problem can also be removed with an inner constraint. This does not involve external observations and leads to the so-called free-net solution (Granshaw, 1980;Dermanis, 1994). The theoretical accuracy obtainable with a free-net adjustment, coupled with precise image points and good calibration parameters is superior to 1:100,000.…”

Section: Camera Calibration In Photogrammetry and Computer Visionmentioning

confidence: 93%

Targetless Camera Calibration

Barazzetti

Mussio

Remondino

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Camera Calibration In Photogrammetry and Computer Visionmentioning

confidence: 93%

Targetless Camera Calibration

Barazzetti

Mussio

Remondino

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Three-image matching is much more discriminant than two-image matching, so there should be fewer outliers in a RANSAC run. Also, it is well known in photogrammetry that a triangle of three widely spaced cameras gives much stabler and more reliable geometry estimates than classical two image stereo, essentially because errors within epipolar planes are uncontrollable under stereo [11]. The paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Some Results on Minimal Euclidean Reconstruction from Four Points

2006

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Abstract. Methods for reconstruction and camera estimation from miminal data are often used to boot-strap robust (RANSAC and LMS) and optimal (bundle adjustment) structure and motion estimates. Minimal methods are known for projective reconstruction from two or more uncalibrated images, and for "5 point" relative orientation and Euclidean reconstruction from two calibrated parameters, but we know of no efficient minimal method for three or more calibrated cameras except the uniqueness proof by Holt and Netravali. We reformulate the problem of Euclidean reconstruction from minimal data of four points in three or more calibrated images, and develop a random rational simulation method to show some new results on this problem. In addition to an alternative proof of the uniqueness of the solutions in general cases, we further show that unknown coplanar configurations are not singular, but the true solution is a double root. The solution from a known coplanar configuration is also generally unique. Some especially symmetric point-camera configurations lead to multiple solutions, but only symmetry of points or the cameras gives a unique solution.

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“…This control information can be in the form of a 2D or 3D test field which includes several signalized targets, whose coordinates are either determined from a pre-surveying process or estimated using a free-network adjustment procedure (Granshaw, 1980). Depending on the nature of the utilized test field, convergent images and images in portrait and landscape mode might be required to avoid dependencies between the IOPs and EOPs during the bundle adjustment with selfcalibration procedure.…”

Section: Camera Calibration Parametersmentioning

confidence: 99%

Multi-camera System Calibration with Built-in Relative Orientation Constraints (Part 1) Theoretical Principle

Lari¹,

Habib²,

Mazaheri³

et al. 2014

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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In recent years, multi-camera systems have been recognized as an affordable alternative for the collection of 3D spatial data from physical surfaces. The collected data can be applied for different mapping(e.g., mobile mapping and mapping inaccessible locations)or metrology applications (e.g., industrial, biomedical, and architectural). In order to fully exploit the potential accuracy of these systems and ensure successful manipulation of the involved cameras, a careful system calibration should be performed prior to the data collection procedure. The calibration of a multi-camera system is accomplished when the individual cameras are calibrated and the geometric relationships among the different system components are defined. In this paper, a new single-step approach is introduced for the calibration of a multi-camera system (i.e., individual camera calibration and estimation of the lever-arm and boresight angles among the system components). In this approach, one of the cameras is set as the reference camera and the system mounting parameters are defined relative to that reference camera. The proposed approach is easy to implement and computationally efficient. The major advantage of this method, when compared to available multi-camera system calibration approaches, is the flexibility of being applied for either directly or indirectly geo-referenced multi-camera systems. The feasibility of the proposed approach is verified through experimental results using real data collected by a newly-developed indirectly geo-referenced multi-camera system. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Bundle Adjustment Methods in Engineering Photogrammetry

Cited by 189 publications

References 9 publications

Targetless Camera Calibration

Targetless Camera Calibration

Some Results on Minimal Euclidean Reconstruction from Four Points

Multi-camera System Calibration with Built-in Relative Orientation Constraints (Part 1) Theoretical Principle

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