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.

The fiducial marker system for LiDAR is crucial for the robotic application but it is still rare to date. In this paper, an Intensity Image-based LiDAR Fiducial Marker (IILFM) system is developed. This system only requires an unstructured point cloud with intensity as the input and it has no restriction on marker placement and shape. A marker detection method that locates the predefined 3D fiducials in the point cloud through the intensity image is introduced. Then, an approach that utilizes the detected 3D fiducials to estimate the LiDAR 6-DOF pose that describes the transmission from the world coordinate system to the LiDAR coordinate system is developed. Moreover, all these processes run in real-time (approx 40 Hz on Livox Mid-40 and approx 143 Hz on VLP-16). Qualitative and quantitative experiments are conducted to demonstrate that the proposed system has similar convenience and accuracy as the conventional visual fiducial marker system. The codes and results are available at: https://github.com/York-SDCNLab/IILFM.