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/ .

中文翻译：

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TopoTag：稳健且可扩展的拓扑基准标记系统

基准标记在增强现实 (AR)、机器人导航以及需要相机和物体之间的相对姿态的一般应用中发挥着重要作用。在这里，我们介绍了 TopoTag，这是一个强大且可扩展的拓扑基准标记系统，它支持从单个图像进行可靠和准确的姿态估计。TopoTag 在标记检测中使用拓扑和几何信息来实现更高的鲁棒性。拓扑信息广泛用于二维标记检测，并进一步用于 ID 解码的相应几何信息。通过利用所有 TopoTag 顶点实现稳健的 3D 姿态估计。无需像以前的系统那样为了更高的召回率和精确度而牺牲比特，TopoTag 可以使用完整的比特进行 ID 编码。TopoTag 支持数万个唯一 ID，并可轻松扩展到数百万个唯一标签，从而实现大规模可扩展性。我们收集了一个包含共 169,713 张图像进行评估的大型测试数据集，涉及面内和面外旋转、图像模糊、不同距离和各种背景等。数据集上的实验和真实的室内外场景测试滚动快门相机都表明，在检测精度、顶点抖动、姿态抖动和精度等各种指标方面，TopoTag 明显优于以前的基准标记系统。 此外，只要保持主要标签拓扑结构，TopoTag 就支持遮挡并允许灵活的形状设计，用户可以自定义内部和外部标记形状。我们的标记设计/生成、标记检测的代码，http://herohuyongtao.github.io/research/publications/topo-tag/ .