Multicamera Calibration Optimization Method Based on Improved Seagull Algorithm

There are some problems in the process of camera calibration, such as insufficient accuracy and poor accuracy. Based on the seagull algorithm, the adaptive differential evolution algorithm is combined with the seagull algorithm to optimize the multicamera calibration. The seagull algorithm can achieve good results on multiparameter problems and effectively avoid falling into local optima. In this paper, the adaptive differential search algorithm is adopted to improve the local search ability and optimize the local search and global search ability. According to Zhang Zhengyou's method, the calibrated parameter is obtained, in which the parameter is used as the initial value. Then, taking the minimum mean error as the criterion, the improved seagull algorithm (SOA-SaDE) is used to establish the objective function, and the internal parameters and distortion coefficient of the camera are further solved. Verification experiments showed that the fusion algorithm has less reprojection error and higher calibration accuracy gull algorithm.

Optimization of the stereo system calibration parameters for photogrammetric measurements in the conditions of field experiments

Optical methods for deformation diagnostics and surface shape measurement are often used in scientific research and industry. Most of these methods are based on the triangulation of a set of two-dimensional points from different images corresponding to the three-dimensional points of an object in space. Triangulation is based on the stereo system calibration parameters, which are determined before the experiment. Measurements during conditions with increased vibration loads can lead to a change in the relative position of the cameras of the stereo system (decalibration). This leads to a change in the actual calibration parameters and an increase in the measurement error. This work aims to solve the problem of increasing the measurement accuracy of the photogrammetric method in the case of high vibration loads. For this, it is proposed to use an optimization algorithm for calibration parameters to minimize the reprojection error of three-dimensional points calculated using triangulation. The paper presents the results of a computer simulation of decalibration of a video camera stereo system, an algorithm for optimizing the external parameters of a stereo system, and an assessment of its performance.

Development and Uncertainty Characterization of Rotating 3D Velocimetry using a Single Plenoptic Camera

Rotating 3D velocimetry (R3DV) is a single-camera PIV technique designed to track the evolution of flow over a rotor in the rotating reference frame. A high-speed (stationary) plenoptic camera capable of 3D imaging captures the motion of particles within the volume of interest through a revolving mirror from the central hub of a hydrodynamic rotor facility, a by-product being an undesired image rotation. R3DV employs a calibration method adapted for rotation such that during MART reconstruction, voxels are mapped to pixel coordinates based on the mirror’s instantaneous azimuthal position. Interpolation of calibration polynomial coefficients using a fitted Fourier series is performed to bypass the need to physically calibrate volumes corresponding to each fine azimuth angle. Reprojection error associated with calibration is calculated on average to be less than 0.6 of a pixel. Experimental uncertainty of cross-correlated 3D/3C vector fields is quantified by comparing vectors obtained from imaging quiescent flow via a rotating mirror to an idealized model based purely on rotational kinematics. The uncertainty shows no dependency on azimuth angle while amounting to approximately less than 0.21 voxels per timestep in the in-plane directions and correspondingly 1.7 voxels in the radial direction, both comparable to previously established uncertainty estimations for single-camera plenoptic PIV.

UAV Photogrammetry under Poor Lighting Conditions—Accuracy Considerations

The use of low-level photogrammetry is very broad, and studies in this field are conducted in many aspects. Most research and applications are based on image data acquired during the day, which seems natural and obvious. However, the authors of this paper draw attention to the potential and possible use of UAV photogrammetry during the darker time of the day. The potential of night-time images has not been yet widely recognized, since correct scenery lighting or lack of scenery light sources is an obvious issue. The authors have developed typical day- and night-time photogrammetric models. They have also presented an extensive analysis of the geometry, indicated which process element had the greatest impact on degrading night-time photogrammetric product, as well as which measurable factor directly correlated with image accuracy. The reduction in geometry during night-time tests was greatly impacted by the non-uniform distribution of GCPs within the study area. The calibration of non-metric cameras is sensitive to poor lighting conditions, which leads to the generation of a higher determination error for each intrinsic orientation and distortion parameter. As evidenced, uniformly illuminated photos can be used to construct a model with lower reprojection error, and each tie point exhibits greater precision. Furthermore, they have evaluated whether commercial photogrammetric software enabled reaching acceptable image quality and whether the digital camera type impacted interpretative quality. The research paper is concluded with an extended discussion, conclusions, and recommendation on night-time studies.

Constraints for Time-Multiplexed Structured Light with a Hand-held Camera

Multi-frame structured light in projector-camera systems affords high-density and non-contact methods of 3D surface reconstruction. However, they have strict setup constraints which can become expensive and time-consuming. Here, we investigate the conditions under which a projective homography can be used to compensate for small perturbations in pose caused by a hand-held camera. We synthesize data using a pinhole camera model and use it to determine the average 2D reprojection error per point correspondence. This error map is grouped into regions with specified upper-bounds to classify which regions produce sufficiently minimal error to be considered feasible for a structured-light projector-camera system with a hand-held camera. Empirical results demonstrate that a sub-pixel reprojection accuracy is achievable with a feasible geometric constraints

Automatic Calibration of a Two-Axis Rotary Table for 3D Scanning Purposes

Rotary tables are often used to speed up the acquisition time during the 3D scanning of complex geometries. In order to avoid manual registration of the point clouds acquired with different orientations, automatic algorithms to compensate the rotation were developed. Alternatively, a proper calibration of the rotary axis with respect to the camera system is needed. Several methods are available in the literature, but they only consider a single-axis calibration. In this paper, a method for the simultaneous calibration of both axes of the table is proposed. A checkerboard is attached to the table, and several images with different poses are acquired. An optimization algorithm is then setup to determine the orientation and the locations of the two axes. A metric to assess the calibration quality was also defined by computing the average mean reprojection error. This metric is used to investigate the optimal number and distribution of the calibration poses, demonstrating that the optimum calibration results are achieved when a wider dispersion of the calibration poses is adopted.

Robust RGB-D SLAM Using Point and Line Features for Low Textured Scene

Three-dimensional (3D) reconstruction using RGB-D camera with simultaneous color image and depth information is attractive as it can significantly reduce the cost of equipment and time for data collection. Point feature is commonly used for aligning two RGB-D frames. Due to lacking reliable point features, RGB-D simultaneous localization and mapping (SLAM) is easy to fail in low textured scenes. To overcome the problem, this paper proposes a robust RGB-D SLAM system fusing both points and lines, because lines can provide robust geometry constraints when points are insufficient. To comprehensively fuse line constraints, we combine 2D and 3D line reprojection error with point reprojection error in a novel cost function. To solve the cost function and filter out wrong feature matches, we build a robust pose solver using the Gauss–Newton method and Chi-Square test. To correct the drift of camera poses, we maintain a sliding-window framework to update the keyframe poses and related features. We evaluate the proposed system on both public datasets and real-world experiments. It is demonstrated that it is comparable to or better than state-of-the-art methods in consideration with both accuracy and robustness.

SINGULARITY-FREE BUNDLE ADJUSTMENT

The photogrammetric bundle adjustment is well-behaved in the case of structured aerial imagery looking in the nadir direction. That is less so in the case of ground-level imagery with less structure and potentially looking in any direction. Besides, the cost function based on reprojection errors of tie points is not defined everywhere and exhibits singularities which renders this bundle adjustment process sensitive to initial conditions and outliers. In order to handle difficult configurations without incurring the risks posed by the reprojection function, we propose a new error function that is equivalent to the reprojection error when this error tends to zero, and that enjoys many desirables properties, such as being defined everywhere and being continuous. This allows an easier implementation of a robust bundle adjustment, and incidentally it also allows to solve derivative problems such as triangulating points starting from arbitrary initial positions, or estimating the relative positions of calibrated and oriented cameras starting from arbitrary positions, thus offering a simple solution to the known-orientation structure-from-motion problem.