ArUcOmni: detection of highly reliable fiducial markers in panoramic images

In this paper, we propose an adaptation of marker detection algorithm for panoramic cameras such as catadioptric and fisheye sensors. Due to distortions and non-uniform resolution of such sensors, the methods that are commonly used in perspective images cannot be applied directly. This work is in contrast with the existing marker detection framework: Automatic reliable fiducial markers Under occlusion (ArUco) for a conventional camera. To keep the same performance for panoramic cameras, our method is based on a spherical representation of the image that allows the marker to be detected and to estimate its 3D pose. We evaluate our approach on a new shared dataset that consists of a 3D rig of markers taken with two different sensors: a catadioptric camera and a fisheye camera. The evaluation has been performed against ArUco algorithm without rectification and with one of the rectified approaches based on the fisheye model.