Query: chessboard camera calibration

Candidate papers:

abstract: A method is proposed to calibrate the long wave infrared ultra wide angle camera in this paper. In addition, a novel calibration chessboard is designed and an advanced chessboard corner positioning method is adopted to improve the calibration precision. The designed calibration chessboard can achieve high thermal infrared contrast and exhibits outstanding stability, which is made of a thermoelectric semiconductor refrigeration device. The proposed subpixel corner positioning method can accurately locate the corners on the calibration chessboard according to the characteristics of the infrared image and the checkerboard pattern. Both the principle of the proposed infrared chessboard and the subpixel corner positioning procedure were presented, and the calibration experiment showed that the mean reprojection error and the root mean square error were reduced to 0.32 pixel and 0.39 pixel, respectively. Comparison studies were also performed to verify the calibration effect of the proposed method, and the possibilities of camera calibration error of the proposed method were analyzed.
author_names: Yichao Chen

Fu-Yu Huang

Feng Shi

Bing-qi Liu

Hao Yu
title: Plane chessboard based calibration method for a LWIR ultra wide angle camera.
venue: Applied optics
year: 2019

abstract: Extrinsic calibration of imaging systems containing multi sensors have enabled unprecedented capabilities in data fusion in the fields of computer vision and robotics throughout the past few decades. In this paper, we propose a simple mutual rotation and translation estimation method for a multi sensor system containing six omnidirectional RGB cameras and a common 3D Light Detection and Ranging (LIDAR) sensor using a planar chessboard pattern. We mount the sensors on a specially designed hexagonal plate, while considering each camera LIDAR combination as an independent multi sensor unit. For each unit, we simultaneously capture chessboard images and their three dimensional (3D) point data at a few different orientations. Two dimensional (2D) chess corners are reprojected into 3D space for plane fitting. RANSAC algorithm is applied on LIDAR points before they are used for plane fitting. The mutual rotation between the camera and LIDAR is calculated by aligning the normal vectors of plane fitting results. An arbitrary point from the camera plane is projected on to the LIDAR plane and the distance is minimized to estimate the mutual translation. The accuracy of this proposed method is evaluated through scene fusion experiments.
author_names: Eung-Su Kim

Soon-Yong Park
title: Extrinsic calibration of a camera LIDAR multi sensor system using a planar chessboard
venue: 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN)
year: 2019

abstract: Abstract. This paper presents a system calibration method for a trifocal sensor, which is sensitive to different spectral bands. The trifocal camera system consists of a stereo camera, operating in the visual (VIS) spectrum and a thermal imaging camera, operating in the Long Wave Infrared (LWIR) spectrum. Intrinsic parameters and spatial alignment are determined simultaneously. As calibration target a passive aluminium chessboard is used. Corner detection and subsequent bundle adjustment is done on all synchronized image triplets. The remaining reprojection errors are in the sub pixel range and enable the system to generate metric point clouds, colored with thermal intensities in real time.
author_names: Andre Choinowski

Dennis Dahlke

Ines Ernst

Sebastian Pless

I Rettig
title: AUTOMATIC CALIBRATION AND CO REGISTRATION FOR A STEREO CAMERA SYSTEM AND A THERMAL IMAGING SENSOR USING A CHESSBOARD
venue: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
year: 2019

abstract: Abstract. Geometric camera calibration is a mandatory prerequisite for many applications in computer vision and photogrammetry. Especially when requiring an accurate camera model the effort for calibration can increase dramatically. For the calibration of the stereo camera used for optical navigation a new chessboard based approach is presented. It is derived from different parts of existing approaches which, taken separately, are not able to meet the requirements. Moreover, the approach adds one novel main feature: It is able to detect all visible chessboard fields with the help of one or more fiducial markers simply sticked on a chessboard (AprilTags) This allows a robust detection of one or more chessboards in a scene, even from extreme perspectives. Except for the acquisition of the calibration images the presented approach enables a fully automatic calibration. Together with the parameters of the interior and relative orientation the full covariance matrix of all model parameters is calculated and provided, allowing a consistent error propagation in the whole processing chain of the imaging system. Even though the main use case for the approach is a stereo camera system it can be used for a multi camera system with any number of cameras mounted on a rigid frame.
author_names: Jurgen Wohlfeil

Denis Griessbach

Ines Ernst

Dirk Baumbach

Dennis Dahlke
title: AUTOMATIC CAMERA SYSTEM CALIBRATION WITH A CHESSBOARD ENABLING FULL IMAGE COVERAGE
venue: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
year: 2019

abstract: Cameras and LiDARs (Light Detection and Ranging) are often equipped for autonomous driving applications, and need to be calibrated together to unify the coordinates. Conventional calibration approaches need complex calibration boards which are inconvenient to use. This paper proposes a novel and accurate calibration method using a simple rectangle planar chessboard with known size, and the key idea is to use 2D 3D point correspondences in LiDAR and camera frames. The accuracy of the calibration is improved by fusing the information of a lot of corresponding point clouds and images. In addition, the whole calibration process requires less manual intervention. Experiments show that this approach is more accurate compared to a conventional widely used method.
author_names: Shuguang Tu

Yafei Wang

Chenyang Hu

Yuneng Xu
title: Extrinsic Parameter Co calibration of a Monocular Camera and a LiDAR Using Only a Chessboard
venue: Lecture Notes in Electrical Engineering
year: 2019

abstract: Camera calibration is the procedure of identifying camera parameters for rectifying and un distorting the images. In this paper Calibration of Stereo Camera using the set of images taken by 2 cameras (from left and right direction) and study about the stability as well as statically analysis of parameters is carried out by physically changes the angle of camera. For estimating extrinsic parameters the images could be taken in a way, covering whole surface area of the camera sensor i.e. at different depth and orientation but later, it was observed that if the images taken were in a straight line at different depths covering the same area of camera sensor then the repeatability could be seen in the results of extrinsic parameters.
author_names: Megha Simlote

Pratibha Agrawal

Sunil Pathak
title: Repeatable Extrinsic Stereo Camera Calibration by Moving Chessboard in a Straight line
venue: International Journal of Computer Applications
year: 2019

abstract: This work describes a novel approach to localize sub pixel chessboard corners for camera calibration and pose estimation. An ideally continuous chessboard corner model is established, as a function of corner coordinates, rotation and shear angles, gain and offset of grayscale, and blurring strength. The ideal model is evaluated by a low cost and high similarity approximation for sub pixel localization, and by performing a nonlinear fit to input image. A self checking technique is also proposed by investigating qualities of the model fits, for ensuring the reliability of addressing perspective n point problem. The proposed method is verified by experiments, and results show that it can share a high performance. It is also implemented and examined in a common vision system, which demonstrates that it is suitable for on site use.
author_names: Tianlong Yang

Qian-Cheng Zhao

Xian Wang

Quan Zhou
title: Sub Pixel Chessboard Corner Localization for Camera Calibration and Pose Estimation
year: 2018

abstract: This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboard's patterns. Powell's method is introduced for resolving the discontinuity problem in optimization. The corners of the fitted model are considered as the 3D corners of the chessboard. Once the corners of the chessboard in the 3D point cloud are estimated, the extrinsic calibration of the two sensors is converted to a 3D 2D matching problem. The corresponding 3D 2D points are used to calculate the absolute pose of the two sensors with Unified Perspective n Point (UPnP) Further, the calculated parameters are regarded as initial values and are refined using the Levenberg Marquardt method. The performance of the proposed corner detection method from the 3D point cloud is evaluated using simulations. The results of experiments, conducted on a Velodyne HDL 32e LiDAR and a Ladybug3 camera under the proposed re projection error metric, qualitatively and quantitatively demonstrate the accuracy and stability of the final extrinsic calibration parameters.
author_names: Weimin Wang

Ken Sakurada

Nobuo Kawaguchi
title: Reflectance Intensity Assisted Automatic and Accurate Extrinsic Calibration of 3D LiDAR and Panoramic Camera Using a Printed Chessboard
venue: Remote. Sens.
year: 2017

author_names: Yeon-Serk Yu
title: Effect of Chessboard Image Noise on Camera Calibration
year: 2018

abstract: We introduce an algorithm that detects chessboard patterns in images precisely and robustly for application in camera calibration. Because of the low requirements on the calibration images, our solution is particularly suited for endoscopic camera calibration. It successfully copes with strong lens distortions, partially occluded patterns, image blur, and image noise. Our detector initially uses a sparse sampling method to find some connected squares of the chessboard pattern in the image. A pattern growing strategy iteratively locates adjacent chessboard corners with a region based corner detector. The corner detector examines entire image regions with the help of the integral image to handle poor image quality. We show that it outperforms recent solutions in terms of detection rates and performs at least equally well in terms of accuracy.
author_names: Mathis Hoffmann

Andreas Ernst

Tobias Bergen

Sebastian Hettenkofer

Jens-Uwe Garbas
title: A Robust Chessboard Detector for Geometric Camera Calibration
venue: VISIGRAPP
year: 2017