Infrared Camera Geometric Calibration: A Review and a Precise Thermal Radiation Checkerboard Target

Abstract

Different infrared (IR) planar geometric calibration targets have been developed over the years that exploit a well-established and flexible optical camera geometric calibration procedure following the pinhole approximation. This geometric calibration is typically neglected in IR cameras, due to the relatively low resolution of thermal images and the complex IR targets needed for the geometric calibration in comparison to the optical targets. In this study, a thorough literature review of numerous IR camera geometric calibration targets, along with their respective outcomes, were summarized and leveraged to deliver a practical checkerboard target for less experienced end users, while offering the lowest reprojection errors. It was concluded that the fabrication of high emissivity contrast and precise square points of intersection within a checkerboard pattern extends the accuracy of capturing these control points in a thermal image for an optimized IR camera geometric calibration. Accordingly, two simple planar checkerboard targets were fabricated using laser engraving and ultraviolet (UV) printing technologies on a polished stainless steel (SS304) plate. The UV-printed checkerboard target on a polished metallic alloy delivered the lowest mean reprojection error (MRE) of 0.057 pixels and the lowest root mean square error (RMSE) of reprojection of 0.063 pixels, with a standard deviation lower than 0.003 pixels. The UV-printed design offers better accuracy than any other checkerboard calibration target, and comparable results to the best prominent circular pattern results reported in the literature.

Multipath-Closure Calibration of Stereo Camera and 3D LiDAR Combined with Multiple Constraints

“…In order to investigate the influence of different constraints on the calibration results and compare them with existing methods, we employed the same experimental data as in Section 3 to calculate the extrinsic parameters under three cases: coplanar constraint, coplanar constraint + collinear constraint, and coplanar constraint + multipath-closure constraint + collinear constraint. The extrinsic parameters were compared with the results obtained by the method proposed in the literature [14]. Calculating the accuracy indexes according to the back-projection test and Equations ( 34) and (35), the results are presented in Table 2.…”

“…The calibration algorithm proposed in this paper was used to calculate the extrinsic parameters, and the accuracy of the algorithm was calculated using Equation (37). The accuracy comparison results with the current mainstream methods (Unnikrishnan [8], Zhou [14], Toth [43]) are shown in Figure 17. The results of the extrinsic calibration error between the LiDAR and Camera No.2 at different levels of LiDAR noise are similar to those shown in Figure 17.…”

“…The extrinsic calibration between the stereo camera and the 3D LiDAR relies on the geometric correspondence of specific calibration objects between the three sensors. The chessboard calibration object [13][14][15][16][17][18] is the most widely used in this context. Unnikrishnan et al [19], Pandey et al [20], Mirazei et al [21], Liu et al [22], Khoscovian et al [23], Zhou et al [24], Lu et al [25], Liu et al [26], and Li et al [27] established coplanar constraint relationships through checkerboard calibration objects to accomplish the extrinsic calibration of cameras and 3D LiDAR.…”

Enhanced Methodology for Building Surface Inspection Using Infrared Thermography and Numerical Simulation

“…Furthermore, we provide a summary of the contact point SAVS [20] (Table 3). The data fusion techniques varied, even though the experimental setup was different.…”