With use cases that range from external localisation of single robots or robotic swarms to self-localisation in marker-augmented environments and simplifying perception by tagging objects in a robot's surrounding, fiducial markers have a wide field of application in the robotic world.
We propose a new family of circular markers which allow for both computationally efficient detection, tracking and identification and full 6D position estimation.
At the core of the proposed approach lies the separation of the detection and identification steps, with the former using computationally efficient circular marker detection and the latter utilising an open-ended `necklace encoding', allowing scalability to a large number of individual markers.
While the proposed algorithm achieves similar accuracy to other state-of-the-art methods, its experimental evaluation in realistic conditions demonstrates that it can detect markers from larger distances while being up to two orders of magnitude faster than other state-of-the-art fiducial marker detection methods. In addition, the entire system is available as an open-source package at \url{https://github.com/LCAS/whycon}
