Camera Calibration Research Articles

Remote Sensing Techniques: Hyperspectral Imaging and Data Analysis.

Hyperspectral imaging is a remote sensing technique that enables remote, noninvasive measurement of plant traits. Here, we outline the procedures for camera setup, scanning, and calibration, along with the acquisition of black and white reference materials, which are the key steps in collecting hyperspectral imagery. We also discuss the development of predictive models such as partial least-squares regression, using both large and small datasets, which are used to predict plant traits from hyperspectral data. To ensure practical applicability, we provide code examples that allow readers to immediately implement these techniques in real-world scenarios. We introduce these topics to beginners in an accessible and understandable manner.

Polarizing Camera Array System Equipment and Calibration Method

Polarizing Camera Array System Equipment and Calibration Method

Extrinsic Calibration of Camera and LiDAR Systems With Three‐Dimensional Towered Checkerboards

With the increasing utilization of cameras and three‐dimensional Light Detection and Ranging (LiDAR) systems in perception tasks, the fusion of these two sensor modalities has emerged as a prominent research focus in the fields of robotics and unmanned systems. While various extrinsic calibration methods have been developed, they often suffer from limited accuracy when using low‐resolution LiDAR sensors and require the placement of calibration targets at multiple locations. This paper introduces a novel calibration target known as the Three‐Dimensional Towered Checkerboard (3TC), along with a precise and straightforward extrinsic calibration approach for camera‐LiDAR systems. The 3TC consists of stacked cubes adorned with planar or 2D checkerboards, which provide the known positions of checkerboard corner points in three‐dimensional space. Leveraging the Iterative Closest Point (ICP) algorithm, the proposed method calculates the spatial relationship between LiDAR point cloud data and the 3TC model to infer the positions of checkerboard corner points in the LiDAR coordinate system. Subsequently, the Perspective‐n‐Point (PnP) algorithm is employed to establish the correlation between corner positions in the LiDAR coordinate system and the camera image, given the intrinsic parameters of the camera. By ensuring an adequate number of cubes and 2D checkerboards on a specific 3TC, along with accurately estimated corner point positions in LiDAR, a single frame of data from both the camera and LiDAR facilitates their extrinsic calibration. Experimental validations conducted across diverse camera and LiDAR systems, achieving minimal error close to the theoretical limit of the devices, attest to the robustness and precision of the 3TC and the proposed calibration methodology.

Targetless Extrinsic Calibration of Camera and LiDAR via Identifying True 2-D–3-D Line Matching Among Unknown Line Correspondences of Structured Environments

Targetless Extrinsic Calibration of Camera and LiDAR via Identifying True 2-D–3-D Line Matching Among Unknown Line Correspondences of Structured Environments

Research on the application of machine vision in the design of machine automation

Abstract The application of machine vision technology to mechanical automation design is an inevitable measure to improve the comprehensive productivity of mechanical production manufacturing. In this paper, a mechanical automation system based on SOA architecture is designed to cover the image acquisition, processing, and recognition workflow of an image processing module based on machine vision. The binocular camera calibration is completed by the Zhang Zhengyou calibration method. The image is preprocessed by using algorithms such as the mean value method, segmented linear transformation and median filtering, and an algorithm is proposed to improve the ORB feature point extraction, which can extract and match the feature points quickly and efficiently. Meanwhile, a Harris corner point detection algorithm is proposed to improve the SIFT algorithm to enhance the accuracy of target recognition and localization. The designed mechanical automation system is applied to the lychee picking robot, for example, analysis, which shows that the overall recognition P-R value of the system reaches 0.953, and the classification accuracy is above 0.917. In the mechanical automation route localization, the lateral deviation is lower than 4.80cm. The maximum time for image processing and parameter transfer is 117.966ms, which indicates that the system involved in this paper is relatively stable in operation and has a better effect on the localization of the navigation line, which is of certain application value in the field of mechanical automation design.

High-Precision Camera Calibration Method Based on Subpixel Edge Detection and Circularity Correction Compensation

High-Precision Camera Calibration Method Based on Subpixel Edge Detection and Circularity Correction Compensation

Accurate Event Camera Calibration With Fourier Transform

Accurate Event Camera Calibration With Fourier Transform

Comparative Evaluation and Refinement of Linear Algebra-Based Camera Calibration Algorithms

Comparative Evaluation and Refinement of Linear Algebra-Based Camera Calibration Algorithms

SSFE-M: A Self-Supervised Feature Extraction Model for Enhanced Camera Calibration

SSFE-M: A Self-Supervised Feature Extraction Model for Enhanced Camera Calibration

Affine Constrained Binocular Camera Calibration With Planar Target Single-Axis Translating

Affine Constrained Binocular Camera Calibration With Planar Target Single-Axis Translating

Improving Photogrammetry Instrument Performance through Camera Calibration for Precision Digital Manufacturing

Serial-axis robots are being used in precision digital manufacturing for more intensive applications than previously expected. These industrial robots can show global inaccuracy and positional drift errors over time, which affects the quality of manufactured parts. Photogrammetry is the use of multiple cameras to measure objects in 3D environments, and in this study, camera calibration of a photogrammetry tool consisting of a stereo pair of infrared cameras is investigated. Accurate camera calibration is essential for precise measurement and monitoring of industrial robot positioning in 6 degrees of freedom (6DoF) by a photogrammetry tool. Possible areas of improvement in the camera calibration of the photogrammetric system include the calibration routine and the distortion model accuracy, which are explored in this paper. Variations of the calibration routine are tested and compared to the original routine. Routines with more images and even spacing through the node measurement volume are shown to have more repeatable parameter values. Wide-angle lenses are used to minimize setup volume; however, this results in large amounts of distortion towards the image edges. Improved distortion parameters could result in an increased useable image area. A further in-depth investigation is conducted into the accuracy of the distortion model within the camera calibration, which is found to overcorrect for distortion. Conclusions from these investigations can be used to improve camera calibration for the photogrammetric sensor network, ultimately improving the accuracy of the measurement system and the digital manufacturing platform.

Line-Scan Camera Calibration Method Based on Absolute Phase Target

Line-Scan Camera Calibration Method Based on Absolute Phase Target

Alleviating Radial Distortion Effect for Accurate, Iterative Camera Calibration Using Principal Lines

Alleviating Radial Distortion Effect for Accurate, Iterative Camera Calibration Using Principal Lines

Flexible event camera calibration with blinking binary stripes

Flexible event camera calibration with blinking binary stripes

Research on Visual Recognition and Localization Methods for Different Feature Parts

Aimed at the problems of complex industrial site environment, difficult identification of assembly features and low positioning accuracy, a new visual identification and positioning method is proposed, which can well identify and locate parts and products with different characteristics. First, combined filtering is used in preprocessing to repair low-quality images. Then, edge detection and contour finding are performed on the preprocessed image. Finally, the internal and external parameters of the camera are calculated through the camera calibration, and the acquired pixel coordinates are converted into world coordinates. The results show that the combined filtering algorithm has good noise reduction effect and can remove image surface noise, which improves the recognition accuracy for the identification and positioning of different feature parts by the subsequent contour search method, and the algorithm can meet the requirements of the actual positioning accuracy of the assembly.

Measuring Velocity and Discharge of High Turbidity Rivers Using an Improved Near-Field Remote-Sensing Measurement System

Large-scale particle image velocimetry (LSPIV) is a computer vision-based technique renowned for its precise and efficient measurement of river surface velocity. However, a crucial prerequisite for utilizing LSPIV involves camera calibration. Conventional techniques rely on ground control points, thus restricting their scope of application. This study introduced a near-field remote-sensing measurement system based on LSPIV, capable of accurately measuring river surface velocity sans reliance on ground control points. The system acquires gravity-acceleration data using a triaxial accelerometer and converts this data into a camera pose, thereby facilitating swift camera calibration. This study validates the system through method verification and field measurements. The method verification results indicate that the system’s method for retroactively deriving ground control-point coordinates achieves an accuracy exceeding 90%. Then, field measurements were performed five times to assess the surface velocity of the Datong River. These measured results were analyzed and compared with data collected from the radar wave velocity meter (RWCM) and the LS1206B velocity meter. Finally, a comprehensive sensitivity analysis of each parameter was conducted to identify those significantly impacting the river’s surface velocity. The findings revealed that this system achieved an accuracy exceeding 92% for all river surface velocities measured.

Grasp and Inspection of Mechanical Parts based on Visual Image Recognition Technology

In the process of industrial production, whether the machine can work stably is an important issue related to production efficiency and production safety. The cleaning of mechanical parts is an important factor affecting the stable operation of mechanical equipment, so the cleaning of mechanical parts is one of the important manufacturing links before assembly and manufacturing. The important value of the application of image recognition technology in the quality inspection of mechanical parts is briefly analyzed, and then the common problems in the quality inspection of mechanical parts are studied, the relevant measures of the application of image recognition technology in the quality inspection of mechanical parts are discussed, and finally the case analysis of the quality inspection of mechanical parts is carried out. The main purpose is to rationally use image recognition technology to complete the quality inspection of mechanical parts, so as to improve the overall level of production automation and give full play to the maximum role of this technology. At present, a variety of mechanical parts classification methods based on deep learning are studied. According to the demand of industrial machine parts, the corresponding machine parts data set is created. According to the production requirements, the SPP structure in the traditional YOLOV5 network is improved, and a new SPC structure is proposed: The YOLOv5 network with SPC structure is characterized by no pooling layer, which improves the accuracy and recall rate compared with the traditional YOLOv5 network. Secondly, the control method of robot grasping mechanical parts is studied. Using the camera to capture the image and the upper computer to calculate the mechanical parts under the camera coordinate system; Through camera calibration and hand-eye calibration, the transformation relationship between the grasping coordinates of mechanical parts in the camera coordinate system and the grasping coordinates of mechanical parts in the robot base coordinate system is obtained. The grasping coordinates and part type information of the mechanical parts in the robot base coordinate system are transmitted from the host computer to the robot through Modbus/TCP communication protocol.

A comparative study of pose estimation algorithms for visual navigation in autonomous robots

Autonomous navigation is a research field that gives mobile robots the capacity to perform various tasks without human assistance. Autonomous navigation based on visual sensors can be used in GPS denied environments. Vision-based navigation performs feature detection, matching, and pose estimation using camera images. This paper presents a new approach to autonomous navigation for mobile robots using a Color-based Image Segmentation and Centroid Detection algorithm instead of traditional feature detection algorithms. The algorithm intentionally matches features across images using known feature points and uses conventional techniques such as Epipolar Geometry and Perspective-N-Points algorithms for camera pose estimation. The study includes camera calibration to estimate intrinsic parameters and their physical unit conversion ensuring accurate measurements. Experimental datasets are used to analyze the performance of the Epipolar geometry, Perspective-3-Point, and Efficient-Perspective-n-Point based pose estimation algorithms. To enhance accuracy, the P3P algorithm is modified to consider combinations of image points for pose estimation. The paper concludes with a comparative analysis between the original P3P algorithm and the modified version, providing valuable insights into their respective performances. Overall, the paper aims to provide a comparison of different pose estimation algorithms used for visual navigation. Keywords: pose estimation, visual navigation, camera calibration, perspective-n-point, efficient pnp, epipolar geometry Abbreviations PnP, perspective-n-point; P3P, perspective-3-point; EPnP, efficient perspective-n-point; SLAM, simultaneous localization and mapping; VO, visual odometry; GPS, global positioning system; SURF, speeded up robust features; SIFT, scale-invariant feature transform; EKF, extended kalman filter; RGB, red-green-blue; HSV, hue- saturation-value; CNN, convolutional neural network

An easy and direct protocol based on planar molecular images to quantify 131I using thyroid phantom

A planar nuclear medicine image can be used to estimate dosimetry during iodine therapy. To this end, radionuclide activity distribution should be quantified in the patient’s body in terms of a calibration coefficient. This coefficient allows the net counts to correlate with the image’s activity. This study aims propose a simple and easy calibration protocol to quantify 131I activity in thyroid phantom by molecular planar images. Were acquired 13 planar images of different phantoms: thyroid phantom of symmetrical lobes; thyroid phantom of asymmetrical lobes; the Jacszack cylinder phantom with a syringe surrounded by air and water, and finally a plastic bottle containing a syringe with radionuclide. We applied the 131I radionuclide in a General Electric gamma camera, model Discovery NM/CT 670 with a high energy general purpose parallel hole collimator above the phantoms positioned at camera bed. The calibration coefficient of the gamma camera and the standard deviation were determined for each phantom; the average calibration coefficient obtained was 0.062±0.006 MBq/cps. The results suggested that the phantoms applied as too the calibration coefficient obtained by them can provides reasonable value for the gamma camera calibration factor for iodine 131, therefore an accurate evaluation of the scattering media as the source detector distance could impose higher variability and uncertainties on results.

Data-Driven Model Predictive Control for Uncalibrated Visual Servoing

This paper addresses the image-based visual servoing (IBVS) control problem with an uncalibrated camera, unknown dynamics, and constraints. A novel data-driven uncalibrated IBVS (UIBVS) strategy is proposed, incorporated with the Koopman-based model predictive control (KMPC) algorithm and the adaptive robust Kalman filter (ARKF). First, to alleviate the need for calibration of the camera’s intrinsic and extrinsic parameters, the ARKF with an adaptive factor is utilized to estimate the image Jacobian matrix online, thereby eliminating the laborious camera calibration procedures and improving robustness against camera disturbances. Then, a data-driven MPC strategy is proposed, wherein the unknown nonlinear dynamic model is learned using the Koopman operator theory, resulting in a linear Koopman prediction model. Only input–output data are used to construct the prediction model, and hence, the proposed approach is robust against model uncertainties. Furthermore, with a symmetric quadratic cost function, the proposed approach solves the quadratic programming problem online, and visibility constraints as well as joint torque constraints are taken into account. As a result, the proposed KMPC scheme can be implemented in real time, and the UIBVS performance degradation which arises from the control torque constraints can be avoided. Simulations and comparisons for a 2-DOF robotic manipulator demonstrate the feasibility of the proposed approach. Simulation results further validate that the computation time of the proposed approach is comparable to the one of kinematic-based methods.