A Practical Calibration Method for Cameras and Multiple Line-Lasers in Light Sectioning Systems for Underwater Environments

Camera calibration plays an important role in the field of machine vision and photogrammetry, and among the practical calibration methods, the one proposed by Zhang ZhengYou is higher accuracy and easily operated. However, this method needs to move the camera (or the planar target) to get three or more target images at different locations, and it is better to uniformly fill the calibration target in whole measurement volume to improve the calibrate precision. But manual movement and placement of the targets frequently will increase the difficulty in guaranteeing the uniform distribution of target. In view of this situation and according to the linear imaging model of the camera, a new camera calibration method based on the virtual planar targets is proposed in this paper. A liquid crystal display was used as a target plane, and the 2D target graphics were displayed on this LCD screen. Using TSai's camera calibration method to get initial parameters, a serial of images of the virtual planar targets in different positions were captured with keeping the display position unchanged and are used to calculate the internal and external parameters of the camera by classic Zhang's camera calibration method, and the new internal and external parameters would again guide the movement of virtual target. After several iterations, camera parameters can be obtained with high precision. The presented method is flexible and easy to operate, and it has been applied to calibrate different cameras and an actual 3D shape measurement system in our Lab. The comparison results of the transverse coordinates in plane calculated by this method and by Zhang's camera calibration method shows that this proposed method is quite accurate and reliable.

Compared to a conventional fixed focus lens, a zoom lens is more flexible and adaptable. However, the challenges involved in precise calibration for a zoom camera prevent its widespread use in close-range photogrammetry. A practical calibration method for a zoom camera is proposed. The zoom-focus model is established through dimension reduction of the setting variables, which is represented as a set of functions of the zoom setting. The zoom and focus settings are updated in real time for objects at different measurement depths. The calibration process only requires the zoom camera to observe the control points distributed in the designed calibration field with several combinations of zoom and focus settings. All the coefficients of the zoom-focus model can be solved by a nonlinear joint optimization. Experimental results have proved that the proposed method is effective for close-range photogrammetry.

Practical and stable channel calibration method is one of the most interesting technology for array radar engineers. This paper proposes a single source active calibration method (SAM) for ultra high frequency (UHF) radar with uniform linear array (ULA) Yagi antennas. This SAM measures the amplitude and phase errors of receiving channel at a known direction of arrival (DOA) in the antenna pattern measurement (APM) mode of radar without a transponder, then the calibration values at other angles are determined by a formula derived from array signal model. The results of simulation and field experiment show that (1) the antenna pattern (AP) caculated from SAM calibration is consistent with the simulated and measured AP of the Yagi array. (2) after calibrated by SAM values, DOA estimantion of digital beamforming (DBF) and signal to noise ratio (SNR) gains of Doppler power for 5 ship targets are in good agreement with the theoretical performance of 8 arrays. and (3) the azimuth angle of the single active source is insignificant and can be user-defined according to the field environment. It is proved that the proposed SAM calibration provides a feasibility and idea for the practical and reliable calibration of UHF Yagi ULA.

This paper proposes a calibration method for catadioptric camera systems consisting of a mirror whose reflecting surface is the surface of revolution and a perspective camera as typified by HyperOmni Vision. The proposed method is based on conventional camera calibration and mirror posture estimation. Many methods for camera calibration have been proposed and during the last decade, methods for catadioptric camera calibration have also been proposed. The main problem with catadioptric camera calibration is that the degree of freedom of mirror posture is limited or the accuracy of the estimated parameters is inadequate due to nonlinear optimization. On the other hand, our method can estimate five degrees of freedom of mirror posture and is free from the volatility of nonlinear optimization. The mirror posture has five degrees of freedom, because the mirror surface has a surface of revolution. Our method uses the mirror boundary and can estimate up to four mirror postures. We apply an extrinsic parameter calibration method based on conic fitting for this estimation method. Because an estimate of the mirror posture is not unique, we also propose a selection method for finding the best one. By using the conic-based analytical method we can avoid the initial value problem arising from nonlinear optimization. We conducted experiments on synthesized images and real images to evaluate the performance of our method, and discuss its accuracy.

This paper was performed to find a practical error calibration method for MIMU to overcome the shortcoming of the traditional way. To study it, first, we simplified the error model of the MIMU. The model contains zero bias, scale factors and installation errors of MEMS accelerometer and MEMS gyro. Then, based on the model, we calibrated the accelerometer in use of a practical six-position testing. And in view of the installation error between the turntable and MIMU used in gyro testing, we found a simple method to calibrate the MEMS gyro. The method only need a turntable with one rotation axis. To check the accuracy of the calibration method, for accelerometer, we compared the tilt angle solved by the accelerometer after calibration with the angle before calibration. And for gyro, we compared the output of the gyro after calibration with before. As a result, this method has strength on high precision, easy operation, low cost and strong versatility.

Calibrating roadside camera is essential and indispensable for intelligent traffic surveillance systems. Due to the characteristics of the traffic scenes, the traditional camera calibration methods based on calibration patterns are no longer suitable, since there are generally no calibration patterns (e.g. checkerboard) in traffic scenes. In this paper, we propose a simple and practical calibration method for roadside camera, where the vanishing point in the traffic road direction and the vertical vanishing point are employed that can be easily obtained from most traffic scenes. By making full use of video information, the multiple observations of two vanishing points are available. In order to obtain more accurate calibration results, we present a dynamic calibration method that employs these observations to correct camera parameters and substitutes least squares optimization for closed-form computation. The experimental results on real traffic images demonstrate the effectiveness and practicability of the proposed calibration method.

An accurate and practical calibration method for roadside camera using two vanishing points

Calibration techniques played an essential role of improving the pose accuracy of the industrial robot before delivery. Due to the intense competition among the industrial robot market, numerous complicated calibration approaches, which were classified into 3 levels, had been successfully developed. Yet, in practical, level-1 and level-2 calibration, other than higher level, were often used in factory for the sake of cost-effective matter. And most of the researches and applications were focused on enhancing the position accuracy while the orientation accuracy was neglected. Considering the requirement of painting process and efficiency of calibration, we proposed a practical level-2 calibration method for a spray painting robot which was designed by Industrial Robot Research Center Co. Ltd. The measuring system consisted of API laser tracker and an orientation testing End-Effector. After identification and compensation process, we found this method was more effective compares to Zero-Offset method and Direct Calibration method with the relatively competitive consumption time.

Transport Modelling is capital for transportation planning. All-or-Nothing Traffic Assignment is used quite a lot. The model can not be considered valid without passing through validation and calibration process. A validation and calibration method, for the all-or-nothing traffic assignment, need to be developed. The research produced simple practical validation and calibration method. The calculated model traffic volumes are compared against the real traffic volumes. Simple validation method was developed by just setting a maximum allowable error, measured in precentage. Simple calibration method consists of correcting the model traffic volume by correcting the corresponding OD Matrix cell values. The calibration consists of 4 basic tasks : 1. identifying the traffic volume need to be calibrated, 2. identifying corresponding OD matrix cells corresponding to the traffic volume need to be calibrated, 3. distributing traffic volume error to the coresponding OD matrix cells, and 4. developing the calibrated OD matrix. Validation and Calibration are a pair of an iterative process.

Volatile sulfur compounds (VSCs) are key odorous compounds from emissions of various odour sources because they are odorous and generally have very low odour threshold values. Identification and quantification of them through air server-thermal desorber-gas chromatography-sulfur chemiluminescence detector (AS-TD-GC-SCD) become more and more popular, although VSCs can be determined by other detectors. To find a valid, practical and quick calibration method is also an important step in their analytical processes. This study compared three different sample preparation and unity sampling methods using both gas standards (with 10 VSCs balanced in pure nitrogen gas) and liquid standards of 7 VSCs. For liquid standard sample preparation, two solvents (methanol and n-pentane) were tested and their calibration results were compared. The study revealed that the three calibration methods with both manual and dynamic dilution of VSC standard gases can achieve satisfactory calibration results with nice linear regression and correlation coefficient (r2). The dynamic dilution and loop sampling method is recommended because of its better reliability and time-saving processing. For calibration of VSCs with liquid standards, preparing the samples using dissolved VSCs in n-pentane and analysing them using the loop sampling method achieved best calibration results. For dimethyl trisulfide (DMTS), its calibration cannot obtain as good results as other sulfur compounds even using the best performance calibration method.

Camera calibration is an important and fundamental procedure for the application of a vision sensor to 3D problems. Recently many camera calibration methods have been proposed particularly in the area of robot vision. However, the reliability of data used in calibration has been seldomly considered in spite of its importance. In addition, a camera model can not guarantee good results consistently in various conditions. This paper proposes methods to overcome such uncertainty problems of data and camera models as we often encounter them in practical camera calibration steps. By the use of the RANSAC (Random Sample Consensus) algorithm, few data having excessive magnitudes of errors are excluded. Artificial neural networks combined in a two-step structure are trained to compensate for the result by a calibration method of a particular model in a given condition. The proposed methods are useful because they can be employed additionally to most existing camera calibration techniques if needed. We applied them to a linear camera calibration method and could get improved results.

This paper presents a novel method for camera calibration and image alignment of an image fusion system that consists of a thermal and a color camera. The calibration board that consists of a heated metal and an acrylic plate with asymmetric circle grid pattern was developed. The board was used to calibrate intrinsic parameters of the thermal camera and also used to find the homography to align images of the two cameras. The aligned images have been fine adjusted for their misalignment due to difference in projection centers by region of interest (ROI) setting. The visible camera was calibrated separately by using a typical calibration board with an asymmetric circle grid printed paper. The evaluation was performed in two scenarios. The first one is an indoor static scene. And the second one is an outdoor dynamic scene that is a vehicle tracking application by using CAMShift algorithm. The results validate the proposed method with high accuracy.

A Practical Extrinsic calibration method for joint depth and color sensors

The feasibility and accuracy of four-mirror-based monocular stereo vision (FMSV) are related to the system layout and calibration accuracy, respectively. In this study, a spatial light path analysis method and a calibration method are proposed for an FMSV system. As two-dimensional light path analysis cannot fully characterize the imaging parameters, a spatial light path model is proposed, which allows refinement of the system design. Then, considering the relationship between the lens distortion and the imaging depth of field (DoF), a DoF-distortion equal-partition-based model is established. In the traditional calibration method, the optical axis must be perpendicular to the chessboard. Here, an accurate and practical FMSV calibration method without this constraint is proposed based on the above model. Using the proposed spatial light path analysis technique, a high-accuracy, high-portability FMSV system is constructed and calibrated, for which the average error of the vision-reconstructed distance is 0.0298 mm. In addition, robot path accuracy is evaluated by the system and compared to laser-tracker measurement results. Hence, high accuracy of 0.031 mm is determined for the proposed vision system.

Accurate calibration of laparoscopic cameras is essential for enabling many surgical visualization and navigation technologies such as the ultrasound-augmented visualization system that we have developed for laparoscopic surgery. In addition to accuracy and robustness, there is a practical need for a fast and easy camera calibration method that can be performed on demand in the operating room (OR). Conventional camera calibration methods are not suitable for the OR use because they are lengthy and tedious. They require acquisition of multiple images of a target pattern in its entirety to produce satisfactory result. In this work, we evaluated the performance of a single-image camera calibration tool (rdCalib; Percieve3D, Coimbra, Portugal) featuring automatic detection of corner points in the image, whether partial or complete, of a custom target pattern. Intrinsic camera parameters of a 5-mm and a 10-mm standard Stryker® laparoscopes obtained using rdCalib and the well-accepted OpenCV camera calibration method were compared. Target registration error (TRE) as a measure of camera calibration accuracy for our optical tracking-based AR system was also compared between the two calibration methods. Based on our experiments, the single-image camera calibration yields consistent and accurate results (mean TRE = 1.18 ± 0.35 mm for the 5-mm scope and mean TRE = 1.13 ± 0.32 mm for the 10-mm scope), which are comparable to the results obtained using the OpenCV method with 30 images. The new single-image camera calibration method is promising to be applied to our augmented reality visualization system for laparoscopic surgery.