TopoTag: A Robust and Scalable Topological Fiducial Marker System
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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. 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. Without sacrificing bits for higher recall and precision like previous systems, TopoTag can use full bits for ID encoding and supports tens of thousands unique IDs and easily extends to millions and more by adding more bits, thus achieves perfect scalability. We collect a large 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 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 main tag topological structure is maintained and flexible shape design where users can customize inter and outer marker shapes. Our dataset, marker design and detection algorithm are public to the community.
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