  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/.
