ArUcoE: Enhanced ArUco Marker
Oguz Kedilioglu, Tomas Marcelo Bocco, Martin Landesberger, Alessandro Rizzo, Jörg Franke
Abstract
This paper presents a novel fiducial marker type called ArUcoE. It is obtained from a standard ArUco marker by enhancing it with a chessboard-like pattern. With our approach the pose estimation accuracy of any ArUco marker can easily be increased. Further methods to increase the accuracy are analyzed. By applying a subpixel algorithm to the corner regions we are able to locate the corner points within a pixel and overcome the restriction of pixel-level accuracy. A deep-learning-based super-resolution method is used to artificially increase the pixel density in the same regions. Additionally, the effect of using a single and a stereo camera setup on the accuracy is shown.