Checkerboard Target Research Articles

A Binocular Color Line-Scanning Stereo Vision System for Heavy Rail Surface Detection and Correction Method of Motion Distortion.

Thanks to the line-scanning camera, the measurement method based on line-scanning stereo vision has high optical accuracy, data transmission efficiency, and a wide field of vision. It is more suitable for continuous operation and high-speed transmission of industrial product detection sites. However, the one-dimensional imaging characteristics of the line-scanning camera cause motion distortion during image data acquisition, which directly affects the accuracy of detection. Effectively reducing the influence of motion distortion is the primary problem to ensure detection accuracy. To obtain the two-dimensional color image and three-dimensional contour data of the heavy rail surface at the same time, a binocular color line-scanning stereo vision system is designed to collect the heavy rail surface data combined with the bright field illumination of the symmetrical linear light source. Aiming at the image motion distortion caused by system installation error and collaborative acquisition frame rate mismatch, this paper uses the checkerboard target and two-step cubature Kalman filter algorithm to solve the nonlinear parameters in the motion distortion model, estimate the real motion, and correct the image information. The experiments show that the accuracy of the data contained in the image is improved by 57.3% after correction.

Camera calibration based on lightweight fan-shaped target detection and fitness-distance-balance chaotic marine predators algorithm

Camera calibration represents a critical stage in visual measurement, directly impacting the measurement precision. Addressing the limitations of checkerboard and circular targets, resulting in low calibration accuracy and eccentricity errors, this study introduces a camera calibration method that relies on lightweight fan-shaped target detection and Fitness-Distance-Balance Chaotic Marine Predators Algorithm(FDBCMPA). Starting with the improvement of camera calibration targets by refining and designing fan-shaped targets, this paper aims to tackle eccentricity errors through hardware-based solutions. In the process of extracting target feature points, a lightweight object detection algorithm is devised to capture individual fan-shaped pattern positions from images, transitioning from global multi-shape feature detection to localized single-pattern feature extraction. This approach mitigates the distortions inherent in traditional checkerboard targets during global linear feature imaging and partially alleviates interference from background environments and similar features. Following the initial camera calibration, the FDBCMPA is employed to further optimize the calibration outcomes, thereby boosting the calibration method's performance. Experimental findings highlight that the enhanced object detection method in this study attains detection accuracy comparable to mainstream methods, with distinct advantages in detection speed, parameters, and FLOPs, rendering it more suitable for use in camera calibration. Additionally, the FDBCMPA demonstrates improvements in convergence results compared to MPA, PSO, TACPSO, Chimp, and other methods. The final camera calibration results demonstrate an impressive reprojection error of 0.0738 pixels through the utilization of the proposed camera calibration method introduced in this paper, and substantiate the practicality and effectiveness of the calibration method proposed in this study.

A novel camera calibration method based on circle projection model

Camera calibration is one of the important factors affecting measurement accuracy in the field of visual measurement, and the camera calibration method with high precision and strong robustness is the prerequisite for ensuring high-precision visual measurement. Among the commonly used calibration methods, target-based calibration methods have high accuracy and good flexibility. However, the feature extraction of checkerboard targets is prone to noise interference. Circle targets have higher feature extraction accuracy, but will introduce eccentricity errors in the center of the circle projection. In order to eliminate the eccentricity error of circle targets, this paper proposes a camera calibration method based on the circle projection model, which does not rely on the central projection coordinates, but directly constructs the mapping between the circle equation on the target and the elliptic equation in the camera image, and solves the optimal mapping parameters through nonlinear optimization algorithm. Through simulation and experiments, it has been proven that the calibration method based on the circle projection model has high calibration accuracy and anti-noise interference ability, providing a guarantee for high-precision visual measurement.

A Novel Calibration Method of Line Structured Light Plane Using Spatial Geometry.

The line structured light plane calibration method using a plane target cannot produce satisfactory calibration results due to inaccurate positioning of the calibrated points. Field of view noise and sensor noise affect the target light stripe extraction and camera parameter calculation during the calibration process. These factors will cause the calculation of the coordinates of the calibrated point to deviate, and thus affect the light plane calibration. To solve this problem, we propose a new method to calculate the calibrated point based on spatial geometry. Firstly, for the projection line corresponding to the feature point on the light stripe and the corresponding line on the target, a common perpendicular of these two lines above is established, and since the sum of the squares of the distances from the midpoint to the two straight lines is the smallest, the midpoint of the common perpendicular is taken as the calibrated point. Secondly, the target is moved to different positions, and the non-collinear calibrated points are calculated. Finally, the parameters of the light plane are obtained by fitting these calibrated points. This method requires only a checkerboard target, and has a simple calibration process. The experimental results show that the average error of the calibration method proposed in this paper is 0.011 mm, which is less than the 0.031 mm of the calibration method based on the plane target with cross-ratio invariant.

Infrared Camera Geometric Calibration: A Review and a Precise Thermal Radiation Checkerboard Target

Different infrared (IR) planar geometric calibration targets have been developed over the years that exploit a well-established and flexible optical camera geometric calibration procedure following the pinhole approximation. This geometric calibration is typically neglected in IR cameras, due to the relatively low resolution of thermal images and the complex IR targets needed for the geometric calibration in comparison to the optical targets. In this study, a thorough literature review of numerous IR camera geometric calibration targets, along with their respective outcomes, were summarized and leveraged to deliver a practical checkerboard target for less experienced end users, while offering the lowest reprojection errors. It was concluded that the fabrication of high emissivity contrast and precise square points of intersection within a checkerboard pattern extends the accuracy of capturing these control points in a thermal image for an optimized IR camera geometric calibration. Accordingly, two simple planar checkerboard targets were fabricated using laser engraving and ultraviolet (UV) printing technologies on a polished stainless steel (SS304) plate. The UV-printed checkerboard target on a polished metallic alloy delivered the lowest mean reprojection error (MRE) of 0.057 pixels and the lowest root mean square error (RMSE) of reprojection of 0.063 pixels, with a standard deviation lower than 0.003 pixels. The UV-printed design offers better accuracy than any other checkerboard calibration target, and comparable results to the best prominent circular pattern results reported in the literature.

VDI/VDE 2634–1 performance evaluation tests and systematic errors in passive stereo vision systems

In this paper, we explore the sensitivity of the length tests described in the VDI/VDE 2634-1 guideline to systematic errors in a passive stereo vision system. The results reported here are based on a stereo vision system set up on the table of a Cartesian coordinate measuring machine (CMM) and simulations that match the experimental conditions. The stereo vision system is calibrated using a checkerboard pattern resulting in estimates for 26 camera model parameters. Errors in these model parameters cause errors in the triangulated 3D points, thus resulting in length errors evaluated from those points. In the first part of this study, we consider a grid of points in the measurement volume of the stereo vision system and simulate the effect of induced systematic errors in each model parameter to determine the resulting error in the length for all lines (i.e., between all pairs of points) in that grid. We identify the lines that provide the maximum error in the length for each model parameter. Thus, we identify a collection of lines, which we refer to as model-based lines, that provide maximum sensitivity to the model parameters. Also, through simulations, we assess whether the lines defined in the VDI/VDE guideline are sufficient to detect the systematic errors in the stereo vision system. In the second part of this study, we evaluate the performance of the stereo vision system, i.e., assess the effect of errors in the model parameters, by using the CMM as the reference. For this purpose, the CMM is commanded to move to the set of points in the rectangular grid previously considered in the simulations with the stereo vision system tracking a small checkerboard target installed on the ram of the CMM. We compare the VDI/VDE and model-based lines in terms of the experimentally obtained length errors. The studies show that selection of lines based on the model (determined through simulations and verified experimentally) is advantageous because of the superior ability in detecting systematic errors in stereo vision systems.

A virtual binocular line-structured light measurement method based on a plane mirror

A virtual binocular line-structured light measurement method based on a plane mirror is proposed. A checkerboard target and its virtual image in a front coating plane mirror are captured by a camera in a single picture. The target is imaged in several positions to determine the spatial relationship between the camera and plane mirror. The centers of light stripes are matched with its mirror images by the vanishing point constraint between the checkerboard coplanar point and its corresponding mirrored point. By scanning the object on a moving stage, 3D point cloud data can be obtained. Compared with the traditional binocular line-structured light measurement system, the proposed method just needs a single camera, and two images can be obtained in one procedure. It improves the measurement speed, reduces the cost of measurement, and avoids measurement errors caused by non-strict synchronization between the left and right camera in binocular vision. Experimental results demonstrate good performance with the ability to reconstruction surfaces, and measurement accuracy (RMSE = 0.032 mm) can meet the industrial requirement.

Characterization and mitigation of electronic crosstalk on InGaAs PIN 3D flash LiDAR imagers

This research presents methods and results of characterizing and mitigating electronic crosstalk on InGaAs PIN photodiode 3D flash LiDAR imagers, with the goal of significantly simplifying and improving the calibration system design. 3D flash LiDAR detectors use time to digital conversion (TDC) circuits to estimate the time of flight of a pulse when a detection threshold is met. As the underlying TDC circuits require more space and power, these circuits will cause, in high bus loading events, electronic crosstalk. These events are more likely to occur in situations where many detectors simultaneously trigger, something that can occur when viewing a flat object head-on with uniform illumination, thus limiting these sensors to image a full frame due to this simultaneous ranging crosstalk noise (SRCN). Solutions previously devised to mitigate this electronic crosstalk included using a windowed region of interest to mitigate additional noise by preventing triggering on all of the focal plane array (FPA) except the windowed region and using a checkerboard pattern for imaging the full frame. Here the electronic crosstalk is characterized, and mitigated, using a physical checkerboard target, leading to a more compact system design using a spatial light modulator and direct illumination.

Iterative feature detection of a coded checkerboard target for the geometric calibration of infrared cameras

Abstract. The geometric calibration of cameras becomes necessary when images should be undistorted, geometric image information is needed or data from more than one camera have to be fused. This process is often done using a target with a checkerboard or circular pattern and a given geometry. In this work, a coded checkerboard target for thermal imaging cameras and the corresponding image processing algorithm for iterative feature detection are presented. It is shown that, due in particular to the resulting better feature detectability at image borders, lower uncertainties in the estimation of the distortion parameters are achieved.

Toward snapshot correction of 3D flash LiDAR imagers

We present methods enabling rapid non-uniformity and range walk error correction of 3D flash LiDAR imagers that exhibit electronic crosstalk caused by simultaneously triggering too many detectors. This additional electronic crosstalk is referred to as simultaneous ranging crosstalk noise (SRCN). Using a method in which the 3D flash LiDAR imager views a checkerboard target downrange, the SRCN is largely mitigated. Additionally, processing techniques for computing the non-uniformity correction (NUC) and range walk error correction are described; these include an in-situ thermally compensated dark-frame non-uniformity correction, image processing and filtering techniques for the creation of a photo-response non-uniformity correction, and characterization and correction of the range walk error using data collected across the full focal plane array without the need for sampling or windowing. These methods result in the ability to correct noisy test validation data to a range precision of 8.04 cm and a range accuracy of 1.73 cm and to improve the signal-to-noise ratio of the intensity return by 15 to 49 dB. Visualization of a 3D scene corrected by this process is additionally presented.

Online Training of Stereo Self-Calibration Using Monocular Depth Estimation

Stereo imaging is the most common passive method for producing reliable depth maps. Calibration is a crucial step for every stereo-based system, and despite all the advancements in the field, most calibrations are still done by the same tedious method using a checkerboard target. Monocular-based depth estimation methods do not require extrinsic calibration but generally achieve inferior depth accuracy. In this paper, we present a novel online self-calibration method, which makes use of both stereo and monocular depth maps to find the transformation required for extrinsic calibration by enforcing consistency between both maps. The proposed method works in a closed-loop and exploits the pre-trained networks' global context, and thus avoids feature matching and outliers issues. In addition to presenting our method using an image-based monocular depth estimation method, which can be implemented in most systems without additional changes, we also show that adding a phase-coded aperture mask leads to even better and faster convergence. We demonstrate our method on road scenes from the KITTI vision benchmark and real-world scenes using our prototype camera. Our code is publicly available at https://github.com/YotYot/CalibrationNet.

Data-Driven Laser Plane Optimization for High-Precision Numerical Calibration of Line Structured Light Sensors

Line structured light sensor has been applied in various three dimensional (3D) measurement scenes with the advantages of non-contact, low cost and high speed. Its accuracy, which is directly determined by the calibration method, needs to be further improved to fulfill the measuring tasks of precision parts. Here, we proposed a numerical method that can eliminate the errors of the model based methods through two strategies. One is the establishment of the numerical mapping relationship between stripe pixel coordinates and their world coordinates through piecewise cubic interpolation. Corner points of a checkerboard target are used to obtain sufficient interpolating nodes. This target can be manually aligned with the laser plane and the alignment error would be eliminated via the point projection. The other is the data-driven laser plane optimization. The data set of reference interval distance is computed based on the invariance of cross ratio. The optimization model is to minimize the root mean squared error of measured interval distances by adjusting the laser plane coefficients. After the optimization, a higher numerical mapping relationship can be achieved. It bypasses the camera and the distortion models and reaches a calibration error of only 0.005mm. The comparison studies and the measurement of the steps further validate the proposed method.

An Improved Method for the Calibration of a 2-D LiDAR With Respect to a Camera by Using a Checkerboard Target

This paper proposes an improved method for the calibration of a 2D LiDAR or a planar scanning laser rangefinder with respect to a camera, which uses the same checkerboard target as in the camera calibration. To address one of the drawbacks in the LiDAR calibration with a checkerboard, the sensitivity to checkerboard poses, laser points on the checkerboard are obtained from both the originally positioned LiDAR and the same LiDAR vertically rotated from the original position, in the proposed calibration. They construct the additional constraints with the normal vector of the checkerboard, and strengthen the basic point-on-plane constraints in the LiDAR calibration with a checkerboard. However, in practice, the LiDAR rotation can be imprecise. Thus, we further develop the calibration procedure and examine its calibration performance, in the presence of such impreciseness in the LiDAR rotation. A performance analysis is conducted with synthetic noisy data, and it is confirmed that the proposed calibration yields smaller error in the parameter estimation than a conventional baseline calibration method. Subsequently, we examine the calibration performance with real data, where the proposed calibration is carried out by building a calibration system. It is shown that the proposed calibration also has more consistency in the parameter estimation. For further study, we compare the proposed calibration based on point-on-plane constraints with a calibration based on point-to-point constraints, and show that the proposed calibration, with easier data acquisition, yields comparable results.

Calibrating stereo-digital image correlation system using synthetic speckle-pattern calibration target

We describe an alternative stereo calibration method for stereo-digital image correlation (stereo-DIC) using computer-simulated speckle-pattern calibration targets and a DIC-based control points matching technique. The present work refines and extends the recently proposed camera calibration method to stereo calibration, which is an essential part of stereo-DIC measurement. In the proposed calibration method, the calibration target is generated by displaying a synthetic speckle pattern on a commercial tablet. On the simulated speckle pattern, a group of control points is specified with flexible location and number, which is further precisely matched to the captured calibration images using the well-developed subset-based DIC algorithm. These precisely matched control points are subsequently used to calibrate the intrinsic and extrinsic parameters of the stereo-DIC system with stereo calibration algorithm. For better accuracy, the error in the manufacture of the calibration target is also considered and compensated. As a flexible number of control point matches are registered with high accuracy and strong robustness, the presented stereo calibration method using synthetic speckle pattern calibration target offers higher calibration accuracy than that based on a calibration target with a limited number of designated features, such as checkerboard target and circular point target. After being demonstrated by a set of simulated calibration tests, the proposed stereo calibration method was applied to calibrate a real stereo-DIC system, which was then used to measure 3D full-field shapes and displacements. The results confirm the effectiveness and accuracy of the proposed stereo calibration method for stereo-DIC applications.

Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor

We present the sensor concept and first performance and accuracy assessment results of a novel lightweight topo-bathymetric laser scanner designed for integration on Unmanned Aerial Vehicles (UAVs), light aircraft, and helicopters. The instrument is particularly well suited for capturing river bathymetry in high spatial resolution as a consequence of (i) the low nominal flying altitude of 50–150 m above ground level resulting in a laser footprint diameter on the ground of typically 10–30 cm and (ii) the high pulse repetition rate of up to 200 kHz yielding a point density on the ground of approximately 20–50 points/m2. The instrument features online waveform processing and additionally stores the full waveform within the entire range gate for waveform analysis in post-processing. The sensor was tested in a real-world environment by acquiring data from two freshwater ponds and a 500 m section of the pre-Alpine Pielach River (Lower Austria). The captured underwater points featured a maximum penetration of two times the Secchi depth. On dry land, the 3D point clouds exhibited (i) a measurement noise in the range of 1–3 mm; (ii) a fitting precision of redundantly captured flight strips of 1 cm; and (iii) an absolute accuracy of 2–3 cm compared to terrestrially surveyed checkerboard targets. A comparison of the refraction corrected LiDAR point cloud with independent underwater checkpoints exhibited a maximum deviation of 7.8 cm and revealed a systematic depth-dependent error when using a refraction coefficient of n = 1.36 for time-of-flight correction. The bias is attributed to multi-path effects in the turbid water column (Secchi depth: 1.1 m) caused by forward scattering of the laser signal at suspended particles. Due to the high spatial resolution, good depth performance, and accuracy, the sensor shows a high potential for applications in hydrology, fluvial morphology, and hydraulic engineering, including flood simulation, sediment transport modeling, and habitat mapping.

MODELLING WIDE-ANGLE LENS CAMERAS FOR METROLOGY AND MAPPING APPLICATIONS

Abstract. Wide-angle lenses typically offer fields of view greater than 70°, which are utilized in a variety of imaging, mapping, and navigation applications. Wide-angle lenses are commonly modelled using the central perspective model, compensating for lens distortions through a series of additional parameters. The more extreme the distortions, the further the reality of the lens matches the collinearity equations that define the central perspective model. Fisheye lenses are modelled differently because their fields of view are so wide (typically 180°) that the collinearity model is not applicable. This work studied the effects of modelling wide-angle lenses using both the conventional central perspective model and the fisheye model to determine which model best fits the observations and models the distortions more precisely and accurately. These results were produced by generating observations in a dedicated indoor calibration facility at the University of Calgary: an 11 m × 11 m × 4 m field comprising 291 signalized photogrammetry targets. Multiple free-network, self-calibrating bundle adjustments were performed using different models and different cameras. The results of the self-calibrating bundle adjustments were then utilized in a check adjustment on independent sets of check images to validate their accuracy. Two cameras, a Ladybug5 and a GoPro Hero5, were tested. The GoPro was also calibrated using a checkerboard target pattern, and the results were compared to those of the 3D calibration target-field. The results of the bundle adjustments determined that the fisheye model describes the distortions more precisely in both wide-angle camera systems.

An Improvement in the Identification of the Centres of Checkerboard Targets in Point Clouds Using Terrestrial Laser Scanning.

Measurement using terrestrial laser scanning is performed at several stations to measure an entire object. In order to obtain a complete and uniform point cloud, it is necessary to register each and every scan in one local or global coordinate system. One registration method is based on reference points—in this case, checkerboard targets. The aim of this research was to analyse the accuracy of checkerboard target identification and propose an algorithm to improve the accuracy of target centre identification, particularly for low-resolution and low-quality point clouds. The proposed solution is based on the geometric determination of the target centre. This work presents an outline of a new approach, designed by the author, to discuss the influence of the point cloud parameters on the process of checkerboard centre identification and to propose an improvement in target centre identification. The validation of the proposed solutions reveals that the difference between the typical automatic target identification and the proposed method amounts to a maximum of 6 mm for scans of different qualities. The proposed method may serve as an alternative to, or supplement for, checkerboard identification, particularly when the quality of these scans is not sufficient for automatic algorithms.

Calibration and applications of visible imaging cameras on the C-2U advanced beam-driven field-reversed configuration device.

Two filtered, fast-imaging instruments, with radial and axial views, respectively, were used on the C-2U device to visualize line emission from impurities and hydrogenic neutrals. Novel calibration techniques needed to be developed for these instruments because the accelerated pace of C-2U operations precluded access to the interior of the vacuum vessel, targets used in typical calibration methods were not available, and in order to account for effects which have not been sufficiently addressed in the literature. Spatial calibration involved optimizing parameters in a generic camera model: ex situ using a checkerboard target and in situ using the vacuum vessel port geometry. Radiometric calibration was performed ex situ in three stages. First, the camera relative response function was mapped using an algorithm developed for high dynamic-range imaging. Second, the non-uniformity of the optical system was measured using a large LCD monitor with a characterized angular emission pattern. Finally, the absolute photon efficiency of each interference filter was determined using a calibrated uniform radiance source while also accounting for reduction in the filter transmission for off-normal rays. Periodically during the run campaign, line emission from neutral beams fired into a gas target was used as an in situ reference to check for degradation of viewport transmission. One application using calibrated camera data was tomographic reconstruction of passive impurity emission, which provided a sanity check to the excluded-flux radius inferred from wall-mounted magnetic sensors.

Three-Dimensional Identification for Unbalanced Mass of Rotor Systems in Operation

Unbalanced mass identification is important for rotor systems. Current methods normally use sensors, which only detect vibration in two-dimensional (2D) space. Actually, the rotor systems vibrate in three-dimensional directions. In this paper, a non-contact method is developed to identify unbalanced mass of rotor systems in 3D space. A stereo video system with a pair of synchronized high-speed cameras is established and a feature point is employed to replace traditional contact transducer for measurement. Checkerboard target on a vibration table is used to implement dynamic calibration. The proposed method is compared with eddy current method and laser displacement method. The comparison experiments verify the detection ability of the unbalanced mass for the proposed method. Overall, the proposed method can provide more information than 2D detection methods, which has the great potential for fault diagnosis of rotating machinery.

A monocular vision system based on cooperative targets detection for aircraft pose measurement

In this paper, a monocular vision measurement system based on cooperative targets detection is proposed, which can capture the three-dimensional information of objects by recognizing the checkerboard target and calculating of the feature points. The aircraft pose measurement is an important problem for aircraft’s monitoring and control. Monocular vision system has a good performance in the range of meter. This paper proposes an algorithm based on coplanar rectangular feature to determine the unique solution of distance and angle. A continuous frame detection method is presented to solve the problem of corners’ transition caused by symmetry of the targets. Besides, a displacement table test system based on three-dimensional precision and measurement system human-computer interaction software has been built. Experiment result shows that it has a precision of 2mm in the range of 300mm to 1000mm, which can meet the requirement of the position measurement in the aircraft cabin.