Our paper, ‘A versatile high-performance visual fiducial marker detection system with scalable identity encoding’  has won the best paper award at the Symposium on Applied Computing 2017!

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Abstract:
Fiducial markers have a wide field of applications in robotics, ranging from external localisation of single robots or robotic swarms, over self-localisation in marker-augmented environments, to simplifying perception by tagging objects in a robot’s surrounding. We propose a new family of circular markers allowing for a computationally efficient detection, identification and full 3D position estimation. A key concept of our system is the separation of the detection and identification steps, where the first step is based on a computationally efficient circular marker detection, and the identification step is based on an open-ended ‘Necklace code’, which allows for a theoretically infinite number of individually identifiable markers. The experimental evaluation of the system on a real robot indicates that while the proposed algorithm achieves similar accuracy to other state-of-the-art methods, it is faster by two orders of magnitude and it can detect markers from longer distances.

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