CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects
  Pose Estimation Based on Projective Invariants
Shaoan Wang Mingzhu Zhu Yaoqing Hu Dongyue Li Fusong Yuan Junzhi Yu
High-precision pose estimation based on visual markers has been a thriving
research topic in the field of computer vision. However, the suitability of
traditional flat markers on curved objects is limited due to the diverse shapes
of curved surfaces, which hinders the development of high-precision pose
estimation for curved objects. Therefore, this paper proposes a novel visual
marker called CylinderTag, which is designed for developable curved surfaces
such as cylindrical surfaces. CylinderTag is a cyclic marker that can be firmly
attached to objects with a cylindrical shape. Leveraging the manifold
assumption, the cross-ratio in projective invariance is utilized for encoding
in the direction of zero curvature on the surface. Additionally, to facilitate
the usage of CylinderTag, we propose a heuristic search-based marker generator
and a high-performance recognizer as well. Moreover, an all-encompassing
evaluation of CylinderTag properties is conducted by means of extensive
experimentation, covering detection rate, detection speed, dictionary size,
localization jitter, and pose estimation accuracy. CylinderTag showcases
superior detection performance from varying view angles in comparison to
traditional visual markers, accompanied by higher localization accuracy.
Furthermore, CylinderTag boasts real-time detection capability and an extensive
marker dictionary, offering enhanced versatility and practicality in a wide
range of applications. Experimental results demonstrate that the CylinderTag is
a highly promising visual marker for use on cylindrical-like surfaces, thus
offering important guidance for future research on high-precision visual
localization of cylinder-shaped objects. The code is available at:
https://github.com/wsakobe/CylinderTag.