Geometric Calibration of Rotational Vision System for Dynamic Exterior Orientation

Rotational vision system (RVS) is a common type of active vision with only rotational freedom. Typically, the rotational freedom is provided by turntable and pan-tilt-zoom (PTZ). Or eye in hand (EIH) structure in an articulated arm robot. The ideal assumption that rotation axes are perfectly aligned with the coordinate axes of the local camera is mostly violated due to assembling deviations and limitations of manufacturing accuracy. To solve this problem, we propose a generalized deviation model for a specified rotation axis that relates the rotation motion of the platform to the exterior orientation (EO) of the camera. Based on it we put heuristic estimation algorithms through minimizing global reprojection error and fitting a circle in space respectively for rotating platform with or without accurate angle measurements with constrained global optimization. Implemented experiments on a servo pan-tilt turntable validate the accuracy and efficiency of the above models and calibration technique.

A mobile mapping system utilizing a spherical camera

The transportation departments belonging to respective provinces currently collect highway management data with the use of several methods and systems which include visual field inspections, survey methods, aerial photogrammetry, as well as mobile data acquisition systems. Spherical cameras offer an attractive alternative to standard mobile data acquisition devices for highway management systems as they provide full coverage with a single camera. Inclusion of such a camera requires methods of determining relative, interior and exterior orientation information, as well as bore-sight and lever arm determination. Specialized methods of mosaicking[sic] the imagery are also required. This paper focuses on exploring these methods for spherical cameras. Several computer programs were developed to solve for relative, interior, and exterior orientation parameters. It was concluded that a spherical camera can be efficiently utilized for highway data collection and provides full data coverage with a single camera system.

A mobile mapping system utilizing a spherical camera

The transportation departments belonging to respective provinces currently collect highway management data with the use of several methods and systems which include visual field inspections, survey methods, aerial photogrammetry, as well as mobile data acquisition systems. Spherical cameras offer an attractive alternative to standard mobile data acquisition devices for highway management systems as they provide full coverage with a single camera. Inclusion of such a camera requires methods of determining relative, interior and exterior orientation information, as well as bore-sight and lever arm determination. Specialized methods of mosaicking[sic] the imagery are also required. This paper focuses on exploring these methods for spherical cameras. Several computer programs were developed to solve for relative, interior, and exterior orientation parameters. It was concluded that a spherical camera can be efficiently utilized for highway data collection and provides full data coverage with a single camera system.

Orthocorrection of KH-5 ARGON Satellite Imagery of Aral Sea

Declassified Intelligence Satellite Imagery is a unique source of historical environmental data. Three consecutive and overlapping images of the ARGON, the first dedicated mapping mission from 1962 depicting the surrounding of the Aral Sea were orthocorrected using Ground Control Points. As ARGON mission had a frame type camera, a least squares estimation of exterior orientation parameters were estimated using space resection. A modified space resection algorithm was used to estimate (beside the exterior orientation) the camera principal point coordinates and lens distortion correction coefficients. The overall accuracy of the orthocorrected images are in good accordance with the results of other authors.

On-Orbit Calibration of Installation Matrix between Remote Sensing Camera and Star Camera Based on Vector Angle Invariance

To achieve photogrammetry without ground control points (GCPs), the precise measurement of the exterior orientation elements for the remote sensing camera is particularly important. Currently, the satellites are equipped with a GPS receiver, so that the accuracy of the line elements of the exterior orientation elements could reach centimeter-level. Furthermore, the high-precision angle elements of the exterior orientation elements could be obtained through a star camera which provides the direction reference in the inertial coordinate system and star images. Due to the stress release during the launch and the changes of the thermal environment, the installation matrix is variable and needs to be recalibrated. Hence, we estimate the cosine angle vector invariance of a remote sensing camera and star camera which are independent of attitude, and then we deal with long-term on-orbit data by using batch processing to realize the accurate calibration of the installation matrix. This method not only removes the coupling of attitude and installation matrix, but also reduces the conversion error of multiple coordinate systems. Finally, the geo-positioning accuracy in planimetry is remarkably higher than the conventional method in the simulation results.

Investigation of Chromatic Aberration and Its Influence on the Processing of Underwater Imagery

The number of researchers utilising imagery for the 3D reconstruction of underwater natural (e.g., reefs) and man-made structures (e.g., shipwrecks) is increasing. Often, the same procedures and software solutions are used for processing the images as in-air without considering additional aberrations that can be caused by the change of the medium from air to water. For instance, several publications mention the presence of chromatic aberration (CA). The aim of this paper is to investigate CA effects in low-cost camera systems (several GoPro cameras) operated in an underwater environment. We found that underwater and in-air distortion profiles differed by more than 1000 times in terms of maximum displacement and in terms of curvature. Moreover, significant CA effects were found in the underwater profiles that did not exist in-air. Furthermore, the paper investigates the effect of adjustment constraints imposed on the underwater self-calibration and the reliability of the interior orientation parameters. The analysis of the precision shows that in-air RMS values are just due to random errors. In contrast, the underwater calibration RMS values are 3x-6x higher than the exterior orientation parameter (EOP) precision, so these values contain both random error and the systematic effects from the CA. The accuracy assessment shows significant differences.

A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10−4 to 10−8 (a priori known) and 10−7 (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.