Fiducial markers such as QR codes, ArUco, and AprilTag have become very popular tools for labeling and camera positioning. They are robust and easy to detect, even in devices with low computing power. However, their industrial appearance deters their use in scenarios where an attractive and visually appealing look is required. In these cases, it would be preferable to use customized markers showing, for instance, a company logo. This work proposes a novel method to design, detect, and track customizable fiducial markers. Our work allows creating markers templates imposing few restrictions on its design, e.g., a company logo or a picture can be used. The designer must indicate positions into the template where bits will encode a unique identifier for each marker. Then, our method will automatically create a dictionary of markers, all following the same design, but each with a unique identifier. Finally, we propose a method for detecting and tracking the markers even under occlusion, which is not allowed in traditional fiducial markers. The experiments conducted show that the performance of the customizable markers is similar to the best traditional markers systems without significantly sacrificing speed. INDEX TERMSCustomized Markers, Fiducial Markers, ArUco, AprilTag. I. INTRODUCTION 1 Fiducial markers have become a popular and efficient 2 method to solve labeling and monocular localization prob-3 lems at low cost in indoor environments. Their use has 4 spread in a wide variety of fields, such as surgery [18, 6], 5 robot navigation [34, 43], autonomous aerial vehicle land-6 ing [4], augmented reality applications [19], distributed au-7 tonomous 3D printing [46], human cognitive processes [2], 8 the study of animal behaviour [1] and patient positioning 9 in radiotherapy treatments [36] among others. 10 There are several desirable properties a fiducial marker 11 system should have. It must be easy and fast automatically 12 detecting its markers in images. Each marker should have a unique identifier, and it should be possible to estimate its 14 position w.r.t the camera. They should be robustly detected 15 under occlusion, varying lighting conditions, rotation, and 16 scale.
Square markers are a widespread tool to find correspondences for camera localization because of their robustness, accuracy, and detection speed. Their identification is usually based on a binary encoding that accounts for the different rotations of the marker; however, most systems do not consider the possibility of observing reflected markers. This case is possible in environments containing mirrors or reflective surfaces, and its lack of consideration is a source of detection errors, which is contrary to the robustness expected from square markers. This is the first work in the literature that focuses on reflection-aware square marker dictionaries. We present the derivation of the inter-marker distance of a reflection-aware dictionary and propose new algorithms for generating and identifying such dictionaries. Additionally, part of the proposed method can be used to optimize preexisting dictionaries to take reflection into account. The experimentation carried out demonstrates how our proposal greatly outperforms the most popular predefined dictionaries in terms of inter-marker distance and how the optimization process significantly improves them.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.