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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Automated Camera Calibration

Automated Camera Calibration (ACAL) is a computer program that automates the generation of calibration data for camera models used in machine vision systems. Machine vision camera models describe the mapping between points in three-dimensional (3D) space in front of the camera and the corresponding points in two-dimensional (2D) space in the camera s image. Calibrating a camera model requires a set of calibration data containing known 3D-to-2D point correspondences for the given camera system. Generating calibration data typically involves taking images of a calibration target where the 3D locations of the target s fiducial marks are known, and then measuring the 2D locations of the fiducial marks in the images. ACAL automates the analysis of calibration target images and greatly speeds the overall calibration process.

Chen, Siqi↗
NASA NTRS1987

Solutions to the linear camera calibration problem

The general linear camera calibration problem is formulated and several classification schemes for various subcases of this problem are developed. For each subcase, simple solutions are found that satisfy all necessary constraints. The results improve those already in the literature with respect to simplicity, efficiency, and coverage. However, the classification scheme is not exhaustive.

Grosky, William I.↗
NASA NTRS2001

Matching Images to Models: Camera Calibration for 3-D Surface Reconstruction

In a previous paper we described a system which recursively recovers a super-resolved three dimensional surface model from a set of images of the surface. In that paper we assumed that the camera calibration for each image was known. In this paper we solve two problems. Firstly, if an estimate of the surface is already known, the problem is to calibrate a new image relative to the existing surface model. Secondly, if no surface estimate is available, the relative camera calibration between the images in the set must be estimated. This will allow an initial surface model to be estimated. Results of both types of estimation are given.

Morris, Robin D.↗
NASA NTRS1999

A Method to Solve Interior and Exterior Camera Calibration Parameters for Image Resection

An iterative method is presented to solve the internal and external camera calibration parameters, given model target points and their images from one or more camera locations. The direct linear transform formulation was used to obtain a guess for the iterative method, and herein lies one of the strengths of the present method. In all test cases, the method converged to the correct solution. In general, an overdetermined system of nonlinear equations is solved in the least-squares sense. The iterative method presented is based on Newton-Raphson for solving systems of nonlinear algebraic equations. The Jacobian is analytically derived and the pseudo-inverse of the Jacobian is obtained by singular value decomposition.

Samtaney, Ravi↗
NASA NTRSDate not supplied

Unsteady Pressure Sensitive Paint Camera Calibration Improvements

Data collection using unsteady pressure-sensitive paint (uPSP) has been greatly increased in recent large-scale demonstrations. With this significant increase comes challenges to process this magnitude of data. Techniques designed for several thousands of images collected in a lab do not necessarily scale to millions collected in a production-level system. Novel techniques are needed to meet the tighter requirements imposed on computation time, robustness, and accuracy. This paper outlines a number of such techniques and improvements in regards to the camera calibration process.

Camera calibration↗
NASA NTRSDate not supplied

Unsteady Pressure Sensitive Paint Camera Calibration Improvements

New challenges have arisen in processing a significantly increased volume of data collected during recent large-scale demonstrations of unsteady pressure-sensitive paint. Techniques designed for several thousands of images collected in a lab do not necessarily scale to tens of millions collected in a large-scale test. New techniques are needed to meet the tighter requirements on robustness and accuracy that accompany larger wind tunnel models, higher camera resolution, and more run conditions per test. This paper outlines several such techniques and improvements in regard to the camera calibration process.

Camera calibration↗
NASA NTRSDate not supplied

Unsteady Pressure Sensitive Paint Camera Calibration Improvements

New challenges have arisen in processing a significantly increased volume of data collected during recent large-scale demonstrations of unsteady pressure-sensitive paint. Techniques designed for several thousands of images collected in a lab do not necessarily scale to tens of millions collected in a large-scale test. New techniques are needed to meet the tighter requirements on robustness and accuracy that accompany larger wind tunnel models, higher camera resolution, and more run conditions per test. This paper outlines several such techniques and improvements in regard to the camera calibration process.

Camera calibration↗
NASA NTRS1991

Structural stereopsis - Potential for automatic stereo camera calibration

The paper describes the use of extended edge features as a source of primitives for structural stereopsis and considers the design of a system for autonomous camera calibration. It is shown that the structural approach permits greater use of spatial relational constraints, eliminating the coarse-to-fine tracking of point-based algorithms. Experimental results concerning matching and calibration on real images using Laplacian-of-Gaussian contour fragments as primitives in structural stereopsis are presented, and results in graph-theoretic representation and inexact matches, analytical photogrammetry, and other computer vision and image analysis problem domains are examined. Such a system might be used in aerial photogrammetry and cartography, and robotic vision systems; however, the system is still very much under development.

Boyer, Kim L.↗
NASA NTRS2001

A Simple Approach of CCD Camera Calibration for Optical Diagnostics Instrumentation

Solid State array sensors are ubiquitous nowadays for obtaining gross field images in numerous scientific and engineering applications including optical diagnostics and instrumentation. Linear responses of these sensors are often required as in interferometry, light scattering and attenuation measurements, and photometry. In most applications, the linearity is usually taken to be granted without thorough quantitative assessment or correction through calibration. Upper-grade CCD cameras of high price may offer better linearity: however, they also require linearity checking and correction if necessary. Intermediate- or low-grade CCD cameras are more likely to need calibration for linearity . Here, we present two very simple approaches: one for quickly checking camera linearity without any additional setup and one for precisely correcting nonlinear sensor responses. It is believed that after calibration, those sensors of intermediate or low grade can function as effectively as their expensive counterpart.

Cha, Soyoung Stephen↗
NASA NTRS2019

Camera modeling, centroiding performance, and geometric camera calibration on ASTERIA

The Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) is a 10-kg, 6U CubeSat in low-Earth orbit that was able to achieve subarcsecond pointing stability and repeatability. To date, this is the best pointing on a spacecraft of its size. This paper will analyze various aspects of the performance of its key piece of hardware—the payload. First,a model of the optics and imager, which is used to simulate stellar images, will be presented. The imager parameters used in this model were derived from simple ground measurements. Next, a centroiding algorithm is provided and used on the simulated images to predict centroiding performance. These results will be shown to match on-orbit telemetry of centroiding performance, validating the modeling approach. This paper will then describe an approach for and results of a geometric camera calibration algorithm to estimate the focal length,distortion,and alignment parameters. The modeling, analyses, and results presented in this paper provide key information that can be used in a time domain pointing simulation or a frequency-domain pointing error analysis.

Smith, Matthew W.↗
NASA NTRS2004

Science camera calibration for extreme adaptive optics

The nascent field of planet detection has yielded a host of extra-solar planet detections. To date, these detections have been the result of indirect techniques: the planet is inferred by precisely measuring its effect on the host star. Direct observation of extra-solar planets remains a challenging yet compelling goal. In this vein, the Center for Adaptive Optics has proposed a ground-based, high-actuator density extreme A0 system (XAOPI), for a large (~10 m) telescope whose ultimate goal is to directly evidence a specific class of these objects: young and massive planets. Detailed system wave-front error budgets suggest that this system is a feasible, if not an ambitious, proposition. One key element in this error budget is the calibration and maintenance of the science camera wave front with respect to the wave-front sensor which currently as an allowable contribution of ~ 5 nanometers rms. This talk first summarizes the current status of calibration on existing ground-based A0 systems, the magnitude of this effect in the system error budget and current techniques for mitigation. Subsequently, we will explore the nature of this calibration error term, it's source in the non-commonality between the science camera and wave front sensor, and the effect of the temporal evolution of non-commonality. Finally, we will describe preliminary plans for sensing and controlling this error term. The sensing techniques include phase retrieval, phase contrast and external metrology. To conclude, a calibration scenario that meets the stringent requirement for XAOPI will be discussed.

MacIntosh, Bruce↗
NASA NTRSDate not supplied

Camera Calibration and Alignment Metrology at Johnson Space Center’s Electro-Optics Laboratory

It is increasingly common to see spacecraft equipped with cameras for the purpose of navigation. Images are either sent to Earth or processed autonomously on-board to provide information about the vehicle’s position, velocity, and/or attitude. These can be images of stars or celestial bodies for absolute navigation, or images of another spacecraft for relative navigation. While monocular cameras do not provide range information, the images they capture can be processed to determine bearing vectors to target objects within the camera’s field of view. For a camera to be effective in navigation, it must be carefully calibrated and aligned. This involves accurately modeling the optical effects that govern the projection of line-of-sight directions onto the camera’s pixels and determining the camera’s orientation relative to the spacecraft’s reference frame. Engineers at Johnson Space Center’s Electro-Optics Lab regularly perform camera inspection, calibration, and alignment metrology. This was done for the Orion Optical Navigation (OpNav) Camera, the Orion Docking Camera (DCAM), and for numerous cameras belonging to commercial partners. The nature of optical navigation means that cameras must be well-calibrated and their attitude well understood to provide high accuracy bearing measurements to the navigation filter. The stringent accuracy requirements for Orion could not have been met using traditional checkerboard camera calibration or by simply relying on design drawings. This paper details the hardware, software, techniques, and algorithms used by the EOL team to achieve this level of accuracy.

Paul D Mckee↗
NASA NTRSDate not supplied

Camera Calibration and Alignment Metrology at Johnson Space Center’s Electro-Optics Laboratory

It is increasingly common to see spacecraft equipped with cameras for the purpose of navigation. Images are either sent to Earth or processed autonomously on-board to provide information about the vehicle’s position, velocity, and/or attitude. These can be images of stars or celestial bodies for absolute navigation, or images of another spacecraft for relative navigation. While monocular cameras do not provide range information, the images they capture can be processed to determine bearing vectors to target objects within the camera’s field of view. For a camera to be effective in navigation, it must be carefully calibrated and aligned. This involves accurately modeling the optical effects that govern the projection of line-of-sight directions onto the camera’s pixels and determining the camera’s orientation relative to the spacecraft’s reference frame. Engineers at Johnson Space Center’s Electro-Optics Lab regularly perform camera inspection, calibration, and alignment metrology. This was done for the Orion Optical Navigation (OpNav) Camera, the Orion Docking Camera (DCAM), and for numerous cameras belonging to commercial partners. The nature of optical navigation means that cameras must be well-calibrated and their attitude well understood to provide high accuracy bearing measurements to the navigation filter. The stringent accuracy requirements for Orion could not have been met using traditional checkerboard camera calibration or by simply relying on design drawings. This paper details the hardware, software, techniques, and algorithms used by the EOL team to achieve this level of accuracy.

Paul McKee↗
NASA NTRS1997

Application of Least-Squares Adjustment Technique to Geometric Camera Calibration and Photogrammetric Flow Visualization

Flow visualization produces data in the form of two-dimensional images. If the optical components of a camera system are perfect, the transformation equations between the two-dimensional image and the three-dimensional object space are linear and easy to solve. However, real camera lenses introduce nonlinear distortions that affect the accuracy of transformation unless proper corrections are applied. An iterative least-squares adjustment algorithm is developed to solve the nonlinear transformation equations incorporated with distortion corrections. Experimental applications demonstrate that a relative precision on the order of 40,000 is achievable without tedious laboratory calibrations of the camera.

Chen, Fang-Jenq↗