The Influence of Autofocus Lenses in the Camera Calibration Process (2402.04686v1)

Abstract: Camera calibration is a crucial step in robotics and computer vision. Accurate camera parameters are necessary to achieve robust applications. Nowadays, camera calibration process consists of adjusting a set of data to a pin-hole model, assuming that with a reprojection error close to cero, camera parameters are correct. Since all camera parameters are unknown, computed results are considered true. However, the pin-hole model does not represent the camera behavior accurately if the focus is considered. Real cameras change the focal length slightly to obtain sharp objects in the image and this feature skews the calibration result if a unique pin-hole model is computed with a constant focal length. In this paper, a deep analysis of the camera calibration process is done to detect and strengthen its weaknesses. The camera is mounted in a robot arm to known extrinsic camera parameters with accuracy and to be able to compare computed results with the true ones. Based on the bias that exist between computed results and the true ones, a modification of the widely accepted camera calibration method using images of a planar template is presented. A pin-hole model with distance dependent focal length is proposed to improve the calibration process substantially

• The paper demonstrates that ignoring dynamic focal adjustments in autofocus cameras leads to systematic calibration errors.
• It introduces a methodology that isolates intrinsic parameters during iterative nonlinear minimization for enhanced calibration accuracy.
• The refined approach significantly reduces bias in computed camera positions, benefiting applications with variable camera-to-object distances.

Overview of the Camera Calibration Process

Camera calibration stands as an essential step within the fields of robotics and computer vision, providing a necessary foundation for applications requiring precise spatial understanding between cameras and their environment. The conventional approach relies on the pin-hole camera model adjusted with reprojection error minimization to ascertain camera parameters. However, as detailed in this paper, the pin-hole model inaccurately represents calibration when lens focusing behavior is taken into account.

Camera Focus Impact on Calibration

The standard model falters due to its failure to consider the camera's adaptive process to maintain focus, which in real-world scenarios involves slight alterations to the lens's focal length. Cameras modify their focal length to ensure objects remain in sharp focus within the image, affecting the calibration's precision. Traditional calibration processes, assuming a static focal length, lead to systematic errors as they overlook this dynamic aspect inherent to real cameras. A robot-mounted camera allowed accurate extrinsic parameter measurements, facilitating a comparative analysis with computed results. This comparison unveiled consistent discrepancies between the presumed and actual parameters, rooted in the assumption that static focal length would suffice.

Proposed Methodology for Enhanced Camera Calibration

The paper proposes a more refined calibration approach, analyzing and compensating for the interdependence between camera parameters. In the suggested technique, intrinsic parameters like focal length are isolated and fixed during iterative nonlinear minimization, enabling a more tailored adjustment that eschews the coupling-induced inaccuracies predominant in existing methods. Indeed, observational results endorse the efficiency of the proposed calibration method; it effectively delineates distance-dependent focal length changes, thus improving overall parameter estimation.

Quantitative Findings and Implications

Quantitative analysis underscored the significance of isolating intrinsic camera parameters during calibration. The presented method notably minimized the bias in computed camera locations, highlighting its superior performance over conventional processes. These findings maintain particularly strong implications for applications where camera-to-object distances are variable or unspecified, necessitating a calibration model that can dynamically adjust to variations in focal length induced by the focusing mechanism.

Conclusion

In conclusion, this paper illustrates the limitations of traditional camera calibration methods under the influence of dynamic focal adjustments due to camera focusing. It introduces a modified calibration method that acknowledges these variations, yielding a more accurate set of camera parameters. This nuanced understanding consequently enhances the application of camera calibration in both robotic and computer vision systems, leading to more robust and reliable spatial interpretations.