Automatic Camera System Calibration With a Chessboard Enabling Full Image Coverage

Abstract

<p><strong>Abstract.</strong> 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.</p>

Automatic Calibration and Co-Registration for a Stereo Camera System and a Thermal Imaging Sensor Using a Chessboard

“…Low geometric and radiometric resolution at the chessboard edges likely lead to incompletely detected corners and therefore to inconsistencies between thermal and visual imagery. An approach that overcomes these drawbacks and allows the calibration of a trifocal system as well, is presented in Wohlfeil et al (2019).…”

“…Corner Detection and Bundle Adjustment is done on all image triplets at once. For the automated detection of chessboard corners, as well as the following bundle adjustment, a solution presented in Wohlfeil et al (2019) is used. Basically it works similar to commonly available approaches, but has the aforementioned advantages.…”

Iterative feature detection of a coded checkerboard target for the geometric calibration of infrared cameras

“…Five AprilTag markers are distributed on the checkerboard pattern and placed in such a way that they replace individual black squares of the checkerboard. The algorithm used to detect the corners (Wohlfeil et al, 2019) also differs from the algorithm proposed here (see Sect. 3.2).…”

“…Instead of performing a single calibration with all 60 images, a multicalibration was performed with M subsets of N images. While multicalibration is more common in the photogrammetry community (Luhmann et al, 2013;Wohlfeil et al, 2019) than in the computer vision community, an advantage of it is that statistical parameters such as the standard deviation can be calculated and outliers can be excluded. For a recorded data set, the standard deviation can be used to assess the quality of a calibration in terms of parameter uncertainty.…”

Study of Digital Camera Calibration on a Flat Test Object

“…These methods are based on the application of both spatial and flat test objects, using various mathematical models of sensors (Luhmann T., et al, 2016, Remondino, F. and Fraser, C. S., 2006, Hannemose M., et al, 2019, including neural networks (Fakhri, S. A. and Fakhri, S. A., 2019). Recently, many researchers want to use a flat test object for camera calibration, since it is cheaper than a spatial object and more accessible (Choinowski, A., et al, 2019, Geiger A., et al, 2012, Grammatikopoulos, L., et al, 2019, Tan L., et al, 2017, Wohlfeil, J., et al, 2019. Basically, a chessboard is used as a flat test object in these works.…”