Calibration Board Research Articles

Unmanned aerial vehicle-aided stereo camera calibration for outdoor applications

Monitoring the static and dynamic displacements of large engineering structures, such as buildings and bridges, can provide quantitative information for evaluating structural safety and maintenance purposes. Camera calibration is a key process in vision-based sensor systems for remote displacement measurement. Due to the large field of view of engineering structures, conventional camera calibration methods using precise calibration boards are difficult to apply. A modified calibration method for a binocular stereo vision system based on the epipolar constraint relationship is proposed to simplify the calibration process. Due to the absence of reference points in outdoor applications, an unmanned aerial vehicle that carries a reference marker is adopted. During its flight in the field, sequential images are captured simultaneously with the left and right imaging stations. An alternative to determine the scale factor is also proposed, which provides adequate precision for camera calibration. Two important issues are discussed, including the number of reference points and their selection with regards to the depth of view. The experimental results show that the proposed method is convenient to apply in outdoor situations and can achieve high accuracy in displacement measurement.

Precise positioning of the test surface and screen in a coaxial software configurable optical test system

The coaxial software configurable optical test system (SCOTS) is an aspheric and freeform surface metrology method with high accuracy. The positioning accuracy of the test surface and screen seriously affects the measurement accuracy. In this paper, a novel method, PDCMC (positioning with differential computation, calibration board assisted, 3D measurement and component displacement), is proposed for the precise positioning in the coaxial SCOTS. In the proposed method, a flat crystal is used as the initial test mirror. In the initial system, the positioning of the screen is carried out using the differential computation method. Then, the positioning of a calibration board is carried out based on the feature points on the calibration board. The relative position and attitude among the flat crystal, the calibration board and the actual test surface are provided by a 3D coordinate measuring machine. Finally, through coordinate transformation and data processing, the precise positioning of the test surface and screen can be completed in the actual coaxial SCOTS. The proposed method is highly versatile and suitable for almost all kinds of test surfaces. Simulation and experiments demonstrate the feasibility and accuracy of the proposed method. Compared with the existing universal positioning method, the proposed method has the advantages of fewer required components and higher precision.

Iterative calibration method for measurement system having lens distortions in fringe projection profilometry.

In fringe projection profilometry, system calibration is crucial for guaranteeing the measurement accuracies. Its difficulty lies in calibrating projector parameters, especially when the projector lens has distortions, since the projector, unlike a camera, cannot capture images, leading to an obstacle to knowing the correspondences between its pixels and object points. For solving this issue, this paper, exploiting the fact that the fringe phases on a plane board theoretically have a distribution of rational function, proposes an iterative calibration method based on phase measuring. Projecting fringes onto the calibration board and fitting the measured phases with a rational function allow us to determine projector pixels corresponding to the featured points on the calibration board. Using these correspondences, the projector parameters are easy to estimate. Noting that the projector lens distortions may deform the fitted phase map thus inducing errors in the estimates of the projector parameters, this paper suggests an iterative strategy to overcome this problem. By implementing the phase fitting and the parameter estimating alternately, the intrinsic and extrinsic parameters of the projector, as well as its lens distortion coefficients, are determined accurately. For compensating for the effects of the lens distortions on measurement, this paper gives two solutions. The pre-compensation actively curves the fringes in computer when generating them; whereas when using the post-compensation, the lens distortion correction is performed in the data processing stage. Both methods are experimentally verified to be effective in improving the measurement accuracies.

Dynamic Granularity Matrix Space Model Based High Robust Multi-Ellipse Center Extraction Method for Camera Calibration

Ellipse center extraction is the important basis of camera calibration with circle arrays calibration board, which directly affects the measurement accuracy of machine vision system. Aiming at the problem of high noise sensitivity of the most ellipse center extraction methods, a high robust multi-ellipse center extraction method is proposed. The idea is to transform the problem of multi-ellipse center extraction into the problems of ellipse sub-pixel edge segmentation and edge based ellipse center clustering. Firstly, the flowchart of the proposed method is introduced. Then, the theory of fuzzy quotient space is introduced to establish the dynamic granularity matrix space model, which describes image segmentation problem as the jumping and transformation of the image by hierarchical structure. Finally, an adaptive multi-ellipse sub-pixel edge segmentation method and an optimal ellipse center extraction method are proposed based on the dynamic granularity matrix space model. Experimental results show that the proposed method has higher accuracy, robustness and lower camera calibration error.

Machine Vision-Based Monitoring Methodology for the Fatigue Cracks in U-Rib-to-Deck Weld Seams

The orthotropic steel-box girder (OSG) is widely used in the construction of a large-scale bridges. Since cumulative damages caused by the heavy vehicles and initial flaws of welding, the bridges with OSGs frequently suffer from fatigue cracks, which are commonly distributed around U-ribs. Hence, the management of fatigue cracks is mandatory in practical engineering. Although some techniques have been adopted for the detection of cracks, the workflow is often labor-intensive, time-consuming, and of low-temporal resolution. Considering the optical visibility of a crack and the limitation of the shape of an over-welding-hole around the U-rib, a machine vision-based monitoring methodology for the fatigue cracks in U-rib-to-deck weld seams is proposed in this paper. To be specific, a specific Internet of Things (IoT) based image acquisition device is first developed and introduced to obtain precisely part-view images of a fatigue crack. As followed, a novel image rectification and stitching method based on a specified coded calibration board is innovated and described for generating a measurable panoramic fatigue crack image. Furthermore, a deep learning-based crack detection-segmentation integrated algorithm is developed to detect and segment the crack areas. Afterwards, a feature extraction procedure based on image processing is explored to obtain the morphological features of a crack, involving its area, length and width. Finally, a field experiment was carried out on a real steel suspension bridge. By comparing the measurements both from manual measuring and vision-based monitoring, the results indicate that the proposed methodology is very promising to monitor the fatigue cracks in U-rib-to-deck weld seams, and the root-mean-square errors in length and width measuring could be 3.0195 mm and 0.003 mm, respectively. This work is not only of practical value to the management and maintenance of the OSG bridges in engineering, but also critical for the researches on fatigue cracks propagation.

3D acquisition and stereo-camera calibration by active devices: A unique structured light encoding framework

The use of light encoding techniques is widespread in the field of 3D surface reconstruction. This paper presents a stereo-camera calibration methodology, which integrates structured light encoding with an active digital device. The structured light encoding approach is proposed to unambiguously solve the stereo matching issue for stereo-camera setups. A sequence of vertical and horizontal binary striped patterns, combined with a checkerboard pattern, is displayed by a high-resolution LCD screen, which is used as calibration board. A bundle adjustment technique is adopted to simultaneously adjust both camera parameters and screen geometry, as part of the stereo-camera calibration process, thus taking into account the possible inaccuracies of the digital display. The same structured light approach, with small variants, is projected by a multimedia digital projector to carry out 3D surface reconstruction. The proposed methodology defines a comprehensive framework for the development of a 3D optical scanner, from calibration to 3D acquisition, which has been validated by measuring primitive surfaces and reconstructing free-form shapes with different stereo-camera setups.

High-precision and flexible calibration method of non-overlapping array cameras

In this paper, a high-precision and flexible intrinsic and extrinsic parameters calibration method for non-overlapping array cameras is proposed. In the camera array system, each camera employs an individual planar calibration board to achieve intrinsic parameters calibration. Without assistance of any other coded or un-coded target, the extrinsic parameters of array cameras can be flexibly calibrated based on close-range photogrammetry. To overcome the disadvantages of low precision under a large field of view in close-range photogrammetry, a refinement coordinates mapping (RCM) method to achieve high-precision reconstruction of targets is developed. The principles of single-camera calibration, close-range photogrammetry, and the extrinsic calibration method are introduced, respectively. Experiments are performed to analyze the stability of single-camera calibration and the world coordinates reconstruction of targets. The results of the re-projection error in extrinsic parameters calibration show that the developed RCM method has high precision.

Large object vision measurement system based on a fixed connection between a camera and a total station telescope

Vision measuring, involving the measurement of the three-dimensional shape and color of large objects using stereo vision-based methods, is a challenging task. In this study, a freely adjustable vision measurement system for large objects is developed by connecting a camera to a total station telescope via a fixed mount. In this system, a camera is focused on the target object to be measured in far-range scenarios. A high-precision calibration method is proposed based on non-parametric camera models. In addition, a point cloud-based three-dimensional reconstruction method is developed by integrating with the non-parametric camera models. The experimental results showed that the proposed system has high precision and robustness. When measuring objects from a distance of 8m in an indoor scenario, the error in the maximum spatial distance measurement between two corner points on the calibration board is less than 0.24mm. The maximum relative measurement error is less than 0.09%. When measuring an object 20m away in an outdoor scenario, the average error in the spatial distance measurement between two arbitrary points in an 11 m×4 m testing region is 5.6mm. The average relative error in the distance measurement is 0.12%.

Direct Spherical Calibration of Omnidirectional Far Infrared Camera System

We investigate the calibration of omnidirectional (O-D) infrared (IR) camera for intelligent perception applications. Current O-D camera approaches are primarily focused on O-D color vision applications. The low-resolution O-D IR image edge boundaries are not as sharp as with color vision cameras, and as a result, the standard calibration methods were harder to use and less accurate with the low definition of the O-D IR camera. In order to more fully address O-D IR camera calibration, we propose a new calibration grid center which coordinates control point discovery methodology and a direct spherical calibration (DSC) approach for a more robust and accurate method of calibration. DSC will address the limitations of the existing methods by using the spherical coordinates of the centroid of the calibration board to directly triangulate the location of the camera center and iteratively solve the camera parameters. We compare DSC to three baseline visual calibration methodologies and augment them with additional output of the spherical results for comparison. We also look at the optimum number of calibration boards using an evolutionary algorithm and Pareto optimization to find the best method and combination of accuracy, methodology, and number of calibration boards. The benefits of DSC are both a more efficient calibration board geometry selection approach, and better accuracy than the three baseline visual calibration methodologies.

Camera calibration of a near-parallel imaging system based on magnification rate

Microscopes are now widely used in the field of three-dimensional measurements for their high magnification and changeable physical focal length. However, the small depth of focus restricts the camera from taking clear images with apparent tilts. To set the camera in focus, the calibration board should be near-parallel to the imaging plane. As a result, the traditional calibration methods are not practical for a normal lens, which requires multiple views of the calibration board. For this, a method in which a digital camera with a microscope is calibrated precisely under the near-parallel condition is proposed. In this method, the magnification rate of the imaging system, which is equal to the rate of image distance to object distance, is calculated precisely and utilized to calculate camera parameters. Due to the invariance of the magnification rate, the parameters calculated by this method are accurate and stable. In addition, to solve the problem that the physical focal length cannot be calibrated by one image, the magnification method is adopted to measure it. Finally, the stability and accuracy are evaluated by simulations and experiments.

A magnification-continuous calibration method for SEM-based nanorobotic manipulation systems.

Calibration for scanning electron microscope (SEM) based nanorobotic manipulation systems is important and difficult. Most current calibration methods are cumbersome because they require customized high precision calibration boards and repeated calibration procedures in different magnifications. This paper presents a convenient magnification-continuous calibration method with high precision for SEM-based nanorobotic manipulation systems. The projection matrix containing a continuous magnification factor is obtained by modifying the affine camera model. This facilitates the simplification of the parameter computing process. Movement features are used to align the moving axes of micropositioning stages and calibrate the system, which benefits for the realization of efficient automatic calibration. Three experiments are carried out, and the results demonstrate that the proposed method is effective and practical for calibrating SEM-based nanorobotic manipulation systems under a wide range of continuous magnifications. Experiments also confirm that high precision measurements can be conducted in different magnifications with only once calibration and the relative error is within 1%.

A Calibration Method for a Laser Triangulation Scanner Mounted on a Robot Arm for Surface Mapping.

This paper presents and discusses a method to calibrate a specially built laser triangulation sensor to scan and map the surface of hydraulic turbine blades and to assign 3D coordinates to a dedicated robot to repair, by welding in layers, the damage on blades eroded by cavitation pitting and/or cracks produced by cyclic loading. Due to the large nonlinearities present in a camera and laser diodes, large range distances become difficult to measure with high precision. Aiming to improve the precision and accuracy of the range measurement sensor based on laser triangulation, a calibration model is proposed that involves the parameters of the camera, lens, laser positions, and sensor position on the robot arm related to the robot base to find the best accuracy in the distance range of the application. The developed sensor is composed of a CMOS camera and two laser diodes that project light lines onto the blade surface and needs image processing to find the 3D coordinates. The distances vary from 250 to 650 mm and the accuracy obtained within the distance range is below 1 mm. The calibration process needs a previous camera calibration and special calibration boards to calculate the correct distance between the laser diodes and the camera. The sensor position fixed on the robot arm is found by moving the robot to selected positions. The experimental procedures show the success of the calibration scheme.

Extrinsic calibration of stereo camera and three-dimensional laser scanner

The paper describes an approach that allows solving the problem of extrinsic calibration of a multi-beam lidar and a stereo camera. The approach does not impose any restrictions on the place in which calibration should be performed. Calibration is performed using a calibration board, which is a flat rectangle with special markers. Three-dimensional correspondences are used for calibration. First, a search for the three-dimensional coordinates of the corner points of the calibration board in the coordinate systems of the stereo pair cameras as well as in the coordinate system of the lidar is made. Next, using the optimization methods, calibration parameters are calculated. The results of a series of virtual and real experiments show that the algorithm allows the calibration to be performed with an accuracy comparable to that of sensors. The proposed approach allows one to improve the calibration accuracy due to the simultaneous use of information from two cameras of the stereo pair and is suitable for lidars with both the low and high point density.

A Convenient Calibration Method for LRF-Camera Combination Systems Based on a Checkerboard.

In this paper, a simple and easy high-precision calibration method is proposed for the LRF-camera combined measurement system which is widely used at present. This method can be applied not only to mainstream 2D and 3D LRF-cameras, but also to calibrate newly developed 1D LRF-camera combined systems. It only needs a calibration board to record at least three sets of data. First, the camera parameters and distortion coefficients are decoupled by the distortion center. Then, the spatial coordinates of laser spots are solved using line and plane constraints, and the estimation of LRF-camera extrinsic parameters is realized. In addition, we establish a cost function for optimizing the system. Finally, the calibration accuracy and characteristics of the method are analyzed through simulation experiments, and the validity of the method is verified through the calibration of a real system.

Geometric calibration of markerless optical surgical navigation system.

Patient-to-image registration is required for image-guided surgical navigation, but marker-based registration is time consuming and is subject to manual error. Markerless registration is an alternative solution to avoid these issues. This study designs a calibration board and proposes a geometric calibration method to calibrate the near-infrared tracking and structured light components of the proposed optical surgical navigation system simultaneously. A planar board and a cylinder are used to evaluate the accuracy of calibration. The mean error for the board experiment is 0.035mm, and the diameter error for the cylinder experiment is 0.119mm. A calibration board is reconstructed to evaluate the accuracy of the calibration, and the measured mean error is 0.012mm. A head phantom is reconstructed and tracked by the proposed optical surgical navigation system. The tracking error is less than 0.3mm. Experimental results show that the proposed method obtains high accessibility and accuracy and satisfies application requirements.

Flexible calibration of a stereo vision system by active display

Camera calibration plays a fundamental role for 3D computer vision since it is the first step to recover reliable metric information from 2D images. The calibration of a stereo-vision system is a two-step process: firstly, the calibration of the individual cameras must be carried out, then the two individual calibrations are combined to retrieve the relative placement between the two cameras, and to refine intrinsic and extrinsic parameters. The most commonly adopted calibration methodology uses multiple images of a physical checkerboard pattern. However, the process is time-consuming since the operator must move the calibration target into different positions, typically from 15 to 20. Moreover, the calibration of different optical setups requires the use of calibration boards, which differ for size and number of target points depending on the desired working volume. This paper proposes an innovative approach to the calibration, which is based on the use of a conventional computer screen to actively display the calibration checkerboard. The potential non-planarity of the screen is compensated by an iterative approach, which also estimate the actual screen shape during the calibration process. The use of an active display greatly enhances the flexibility of the stereo-camera calibration process since the same device can be used to calibrate different optical setups by simply varying number and size of the displayed squared patterns.

High-Precision Measurement of Binocular Telecentric Vision System With Novel Calibration and Matching Methods

In stereo vision-based three-dimensional measurements, calibration, and stereo matching are the most challenging tasks for accurate three-dimensional reconstruction. The traditional binocular vision algorithm has low precision, and we propose a binocular vision system using a telecentric lens. In this paper, we propose a calibration and matching algorithm for the telecentric binocular vision system, which collects only two pictures of the calibration plate to complete the system calibration. The algorithm is a special application of 3D reconstruction. In this paper, if the measurement points are selected as points on the grid, the appropriate extension can be used for high-precision 3D reconstruction, which will be explained in detail in the experiments. In order to reduce the calibration error and obtain high maneuverability, the binocular vision system is only calibrated once with a two-dimensional calibration board, and the depth information is obtained through the parallax of the detection process, so as to effectively simplify the calibration process and reduce the errors introduced in the calibration process. For the matching of images in the right and left cameras, the polar theory of telecentric binocular vision system is constructed according to the characteristics of telecentric lens imaging model based on the traditional binocular vision system theory. The matching algorithm only needs to apply the two-dimensional calibration information and then calculates the results according to the parallax combined with the polar line matching algorithm, which simplifies the algorithm flow and reduces the error. In this paper, experiments show that the algorithm proposed is simple and has high precision, good stability, and high-practical value.

Bias Impact Analysis and Calibration of Terrestrial Mobile LiDAR System With Several Spinning Multibeam Laser Scanners

This paper proposes a multiunit light detection and ranging (LiDAR) system calibration procedure to directly estimate the mounting parameters relating multiple spinning multibeam laser scanners to the global navigation satellite system/inertial navigation system (GNSS/INS) unit onboard a mobile terrestrial platform in order to derive point clouds with high-positional accuracy. This procedure is based on the use of conjugate planar/linear features in overlapping point clouds derived from different drive runs. In order to increase the efficiency of semiautomatic conjugate feature extraction from LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within outdoor environments are used in this paper. To ensure the accuracy of the estimated mounting parameters, an optimal configuration of target primitives and drive runs is determined by analyzing the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud for different orientations of target primitives and different drive run scenarios. This impact is also verified experimentally by simulating a bias in each mounting parameter separately. Finally, the optimal configuration is used within an experimental setup to evaluate the performance of the proposed calibration procedure through the a posteriori variance factor of least squares adjustment and the quality of fit of adjusted point cloud to linear/planar surfaces before and after the calibration process. The proposed calibration approach attained an accuracy of 1.42 cm, which is better than the accuracy expected based on the specifications of the involved hardware components, i.e., the LiDAR and GNSS/INS units.

Vehicle counting based on a stereo vision depth maps for parking management

Automated parking management systems provide convenience and efficiency, and such systems are increasingly being deployed in modern urban areas. To facilitate the crucial function of vehicle counting in several applications, we developed a novel mechanism for counting vehicles based on stereoscopic computer vision with depth perception. In this study, we first established depth maps of pairs of images captured using stereo cameras through a scene flow-based approach. Next, we designed a modified sigmoid function to change the histogram distribution in the obtained depth maps by using the disparity threshold estimated from a disparity calibration board. Then, we proposed a vehicle counting mechanism using the modified disparity histogram; this mechanism can be used to easily determine the presence of a vehicle. Consequently, we applied the proposed vehicle detection and counting method to a surveillance camera and used it to determine whether vehicles were approaching an entrance; this camera captured a clear photograph of each license plate, which was then used for automatic recognition. The proposed system was evaluated using nine sets of video data recorded in an indoor parking garage and an outdoor parking lot. The experimental results quantified our method’s high performance and robustness in vehicle counting. For the indoor parking garage, the precision and recall were 99.56% and 98.29%, respectively. For the outdoor parking lot environment, the vehicle counting precision and recall were 98.85% and 98.85%, respectively. Our method was able to avoid counting errors when distinguishing between closely spaced adjacent vehicles.

Binocular stereo vision calibration based on alternate adjustment algorithm

This paper presented a high precision camera calibration algorithm of binocular stereo vision system based on alternative adjustment. This method could be achieved by taking multiple pictures on a professionally designed calibration board in different directions. The watershed algorithm is used to extract feature points, and time domain coding is used to match image pairs. The camera initialization parameters including radial and tangential distortion coefficient are obtained by the classical monocular camera calibration algorithm. We built a binocular vision calibration system which is based on the left and right camera coordinates as the reference coordinate system respectively, and the internal parameters of the two cameras are optimized by alternately adjusting experiments to obtain the optimal value. Then, all the internal parameters are used to optimize the external parameters, so as to obtain the optimum distortion parameters and internal and external parameters. Compared with Zhang’s calibration method, the results show that the mean absolute relative error of the two camera parameters is less than 3.45e−6, and the mean re-projection error of the 10 sets of data is about 0.0725 pixels. The mean re-projection error obtained from the distorted model of Zhang is about 0.0852 pixels. Compared with other calibration methods, the proposed algorithm has higher accuracy and convenient, cost-effective and easy to implement.