An external parameter calibration method for multiple cameras based on laser rangefinder

Measurement ◽  
2014 ◽  
Vol 47 ◽  
pp. 954-962 ◽  
Author(s):  
Zhen Liu ◽  
Fengjiao Li ◽  
Guangjun Zhang
Author(s):  
Mingchi Feng ◽  
Xiang Jia ◽  
Jingshu Wang ◽  
Song Feng ◽  
Taixiong Zheng

Multi-cameras system is widely applied in 3D computer vision especially when multiple cameras are distributed on both sides of the measured object. The calibration methods of multi-cameras system are critical to the accuracy of vision measurement and the key is to find an appropriate calibration target. In this paper, a high-precision camera calibration method for multi-cameras system based on transparent glass checkerboard and ray tracing is described, which is used to calibrate multiple cameras distributed on both sides of the glass checkerboard. Firstly, the intrinsic parameters of each camera is obtained by Zhang’s calibration method. Then, multiple cameras capture several images from the front and back of the glass checkerboard with different orientations, and all images contain distinct grid corners. As the cameras on one side are not affected by the refraction of glass checkerboard, extrinsic parameters can be directly calculated. However, the cameras on another side are influenced by the refraction of glass checkerboard, and the direct use of projection model will produce calibration error. A multi-cameras calibration method using refractive projection model and ray tracing is developed to eliminate this error. Furthermore, both synthetic and real data are employed to validate the proposed approach. The experimental results of refractive calibration show that the error of the 3D reconstruction is smaller than 0.2 mm, the relative errors of both rotation and translation are less than 0.014%, and the mean and standard deviation of reprojection error of 4-cameras system are 0.00007 and 0.4543 pixel. The proposed method is flexible, high accurate, and simple to carry out.


2020 ◽  
Vol 36 (7) ◽  
pp. 1321-1333
Author(s):  
Yongzhi Wang ◽  
Sijing Zhu ◽  
Liu Yuan ◽  
Rui Deng

2021 ◽  
Author(s):  
Wenjun Su ◽  
Junkang Guo ◽  
Zhigang Liu ◽  
Kang Jia

Abstract Rotary-laser automatic theodolite (R-LAT) system is a distributed large-scale metrology system, which provides parallel measurement in scalable measurement room without obvious precision losing. Each of R-LAT emits two nonparallel laser planes to scan the measurement space via evenly rotation, while the photoelectric sensors receive these laser planes signals and performs the coordinate calculation based on triangulation. The accurate geometric parameters of the two laser planes plays a crucial role in maintaining the measurement precision of R-LAT system. Practically, the geometry of the two laser plane, which is termed as intrinsic parameters, is unknown after assembled. Therefore, how to figure out the accurate intrinsic parameters of each R-LAT is a fundamental question for the application of R-LAT system. This paper proposed an easily operated intrinsic parameter calibration method for R-LAT system with adopting coordinate measurement machine. The mathematic model of laser planes and the observing equation group of R-LAT are established. Then, the intrinsic calibration is formulated as a nonlinear least square problem that minimize the sum of deviations of target points and laser planes, and the ascertain of its initial guess is introduced. At last, experience is performed to verify the effectiveness of this method, and simulations are carried out to investigate the influence of the target point configuration on the accuracy of intrinsic parameters.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanwu Zhai ◽  
Haibo Feng ◽  
Yili Fu

Purpose This paper aims to present a pipeline to progressively deal with the online external parameter calibration and estimator initialization of the Stereo-inertial measurement unit (IMU) system, which does not require any prior information and is suitable for system initialization in a variety of environments. Design/methodology/approach Before calibration and initialization, a modified stereo tracking method is adopted to obtain a motion pose, which provides prerequisites for the next three steps. Firstly, the authors align the pose obtained with the IMU measurements and linearly calculate the rough external parameters and gravity vector to provide initial values for the next optimization. Secondly, the authors fix the pose obtained by the vision and restore the external and inertial parameters of the system by optimizing the pre-integration of the IMU. Thirdly, the result of the previous step is used to perform visual-inertial joint optimization to further refine the external and inertial parameters. Findings The results of public data set experiments and actual experiments show that this method has better accuracy and robustness compared with the state of-the-art. Originality/value This method improves the accuracy of external parameters calibration and initialization and prevents the system from falling into a local minimum. Different from the traditional method of solving inertial navigation parameters separately, in this paper, all inertial navigation parameters are solved at one time, and the results of the previous step are used as the seed for the next optimization, and gradually solve the external inertial navigation parameters from coarse to fine, which avoids falling into a local minimum, reduces the number of iterations during optimization and improves the efficiency of the system.


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