Fractal Markers: A New Approach for Long-Range Marker Pose Estimation Under Occlusion

Abstract

Squared fiducial markers are a powerful tool for camera pose estimation in applications such as robots, unmanned vehicles and augmented reality. The four corners of a single marker are enough to estimate the pose of a calibrated camera. However, they have some limitations. First, the methods proposed for detection are ineffective under occlusion. A small occlusion in any part of the marker makes it undetectable. Second, the range at which they can be detected is limited by their size. Very big markers can be detected from a far distance, but as the camera approaches them, they are not fully visible, and thus they can not be detected. Small markers, however, can not be detected from large distances. This paper proposes solutions to the above-mentioned problems. We propose the Fractal Marker, a novel type of marker that is built as an aggregation of squared markers, one into another, in a recursive manner. Also, we proposed a novel method for detecting Fractal Markers under severe occlusions. The results of our experiments show that the proposed method achieves a wider detection range than traditional markers and great robustness to occlusion.