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TopoTag: A Robust and Scalable Topological Fiducial Marker System

IEEE Transactions on Visualization and Computer Graphics, 2020

Fiducial markers have been playing an important role in augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and an object is required. Here we introduce TopoTag, a robust and scalable topological fiducial marker system, which supports reliable and accurate pose estimation from a single image. TopoTag uses topological and geometrical information in marker detection to achieve higher robustness. Topological information is extensively used for 2D marker detection, and further corresponding geometrical information for ID decoding. Robust 3D pose estimation is achieved by taking advantage of all TopoTag vertices. Without sacrificing bits for higher recall and precision like previous systems, TopoTag can use full bits for ID encoding. TopoTag supports tens of thousands unique IDs and easily extends to millions of unique tags resulting in massive scalability. We collected a large test dataset including in total 169,713 images for evaluation, involving in-plane and out-of-plane rotation, image blur, different distances and various backgrounds, etc. Experiments on the dataset and real indoor and outdoor scene tests with a rolling shutter camera both show that TopoTag significantly outperforms previous fiducial marker systems in terms of various metrics, including detection accuracy, vertex jitter, pose jitter and accuracy, etc. In addition, TopoTag supports occlusion as long as the main tag topological structure is maintained and allows for flexible shape design where users can customize internal and external marker shapes. Code for our marker design/generation, marker detection, and dataset are available at http://herohuyongtao.github.io/research/publications/topo-tag/.

Design, Detection and Tracking of Customized Fiducial Markers

IEEE Access

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 TERMS Customized 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.

ARTag, a Fiducial Marker System Using Digital Techniques

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)

Fiducial marker systems consist of patterns that are mounted in the environment and automatically detected in digital camera images using an accompanying detection algorithm. They are useful for Augmented Reality (AR), robot navigation, and general applications where the relative pose between a camera and object is required. Important parameters for such marker systems is their false detection rate (false positive rate), their inter-marker confusion rate, minimal detection size (in pixels) and immunity to lighting variation. ARTag is a marker system that uses digital coding theory to get a very low false positive and inter-marker confusion rate with a small required marker size, employing an edge linking method to give robust lighting variation immunity. ARTag markers are bi-tonal planar patterns containing a unique ID number encoded with robust digital techniques of checksums and forward error correction (FEC). This proposed new system, ARTag has very low and numerically quantifiable error rates, does not require a greyscale threshold as does other marker systems, and can encode up to 2002 different unique ID's with no need to store patterns. Experimental results are shown validating this system.

Marker Detection and Tracking for Augmented Reality Applications

—This paper explores a simple method for detecting and tracking " fiducial " markers in a webcam video stream. The system first uses SIFT feature matching to detect when a marker is present in a frame of the video stream. Then, the detected keypoints are given to a KLT optical flow tracker, which tracks the keypoints frame-by-frame as they move through the video. In this paper, we will describe the method in detail, and present our results and analysis.

On the Design and Evaluation of a Precise, Robust and Scalable Fiducial Marker Framework

International Journal of Pattern Recognition and Artificial Intelligence, 2012

In this paper we present an improved color-based planar fiducial marker system. Our framework provides precise and robust full 3D pose estimation of markers with superior accuracy when compared with many fiducial systems in the literature, while color information encoding enables using over 65 000 distinct markers. Unlike most color-based fiducial frameworks, which requires prior classification training and color calibration, ours can perform reliably under illumination changes, requiring but a rough white balance adjustment. Our methodology provides good detection performance even under poor illumination conditions which typically compromise other marker identification techniques, thus avoiding the evaluation of otherwise falsely identified markers. Several experiments are presented and carefully analyzed, in order to validate our system and demonstrate the significant improvement in estimation accuracy of both position and orientation over traditional techniques.

Rectangular Marker Recognition using Embedded Context Information

ˇˇFiducial markers have been used frequently in augmented reality applications. However, they represent only ID information to identify a marker, and thus they have limitations when we have multiple types of markers. In this paper, we propose a new marker design for augmented reality applications. We embed a marker's context, such as type, size, orientation, and ID, in the barcode that has been used just for storing an ID. BY embedding the context information, it is possible to recognize multiple types of markers at once and to render corresponding contents with proper scale. Our marker design can be used in a mobile augmented reality environment where many unknown types of markers may exist.

A Novel Optical Tracking Algorithm for Point-Based Projective Invariant Marker Patterns

Lecture Notes in Computer Science, 2007

In this paper, we describe a novel algorithm to group, label, identify and perform optical tracking of marker sets, which are grouped into two specific configurations, and whose projective invariant properties will allow obtaining a unique identification for each predefined marker pattern. These configurations are formed by 4 collinear and 5 coplanar markers. This unique identification is used to correctly recognize various and different marker patterns inside the same tracking area, in real time. The algorithm only needs image coordinates of markers to perform the identification of marker patterns. For grouping the dispersed markers that appear in the image, the algorithm uses a "divide and conquer" strategy to segment the image and give some neighborhood reference among markers.

Designing Highly Reliable Fiducial Markers

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010

Fiducial markers are artificial landmarks added to a scene to facilitate locating point correspondences between images, or between images and a known model. Reliable fiducials solve the interest point detection and matching problems when adding markers is convenient. The proper design of fiducials and the associated computer vision algorithms to detect them can enable accurate pose detection for applications ranging from augmented reality, input devices for HCI, to robot navigation. Marker systems typically have two stages, hypothesis generation from unique image features and verification/identification. A set of criteria for high robustness and practical use are identified and then optimized to produce the ARTag fiducial marker system. An edge-based method robust to lighting and partial occlusion is used for the hypothesis stage, and a reliable digital coding system is used for the identification and verification stage. Using these design criteria large gains in performance are achieved by ARTag over conventional ad hoc designs.