Pyramidal Blur Aware X-Corner Chessboard Detector
This addresses the need for faster and more robust calibration in robotics under harsh environments, representing a strong specific gain.
The authors tackled the problem of detecting chessboard fiducial markers in challenging conditions by proposing a new detector that estimates and uses blur to enhance corner localization, achieving an F1-Score of 0.97 and running 1.9x faster than the next fastest method.
With camera resolution ever increasing and the need to rapidly recalibrate robotic platforms in less than ideal environments, there is a need for faster and more robust chessboard fiducial marker detectors. A new chessboard detector is proposed that is specifically designed for: high resolution images, focus/motion blur, harsh lighting conditions, and background clutter. This is accomplished using a new x-corner detector, where for the first time blur is estimated and used in a novel way to enhance corner localization, edge validation, and connectivity. Performance is measured and compared against other libraries using a diverse set of images created by combining multiple third party datasets and including new specially crafted scenarios designed to stress the state-of-the-art. The proposed detector has the best F1- Score of 0.97, runs 1.9x faster than next fastest, and is a top performer for corner accuracy, while being the only detector to have consistent good performance in all scenarios.