Accurate Detection and Localization of Curved Checkerboard-Like Marker Based on Quadratic Form

Abstract

Checkerboard-like markers are widely applied in visual localization applications including SLAM, augmented reality, robot navigation, and 3D scene reconstruction. Most corner detectors assume that the marker is attached to a flat surface, which severely limits the placement of the marker and thus reduces the scope of use for these applications. However, in some scenarios, it is not easy to find an ideal flat surface required by most corner detectors in a given scenario, in which case the marker can often only be fixed on a curved surface. Therefore, the accuracy of most corner detectors may be reduced. In this study, a novel method is proposed with subpixel accuracy to detect and locate the corners on a chessboard-like marker which is either flat or curved. The proposed method fits multi-segment curves in quadratic form to the edges in a checkerboard-like marker. The exact corner positions are considered as the intersections of the corresponding curves with analytical solutions. The proposed method achieves state-of-the-art performance through experiments including synthetic corner localization test, real-world stereo vision triangulation experiment, and pose estimation on a curved object, demonstrating the superiority of our method.