STag: A stable fiducial marker system

Abstract

Fiducial markers provide better-defined features than the ones naturally available in the scene. For this reason, they are widely utilized in computer vision applications where reliable pose estimation is required. Factors such as imaging noise and subtle changes in illumination induce jitter on the estimated pose. Jitter impairs robustness in vision and robotics applications, and deteriorates the sense of presence and immersion in AR/VR applications. In this paper, we propose STag, a fiducial marker system that provides stable pose estimation. STag is designed to be robust against jitter factors, thus sustains pose stability better than the existing solutions. This is achieved by utilizing geometric features that can be localized more repeatably. The outer square border of the marker is used for detection and homography estimation. This is followed by a novel homography refinement step using the inner circular border. After refinement, the pose can be estimated stably and robustly across viewing conditions. These features are demonstrated with a comprehensive set of experiments, including comparisons with the state of the art fiducial marker systems.

Intra-operative Update of Boundary Conditions for Patient-Specific Surgical Simulation

“…Collected images allow to extract the displacement applied to the tissue by the surgeon, by tracking a STag marker attached to the instrument (Fig. 2a) [3]. To evaluate the presented method, point clouds at regular lifting steps of 10 mm are extracted and passed through the complete pipeline described in Section 2.…”

“…RMSE between ground truth point clouds and their corresponding points in the virtual environment when using predicted BCs, at increasing deformation levels [mm]. Acquisitions are grouped by grasping point (A, B, C, D) and initial tissue state(1,2,3). Reported errors are relative to the error at rest and represent the average over 10 runs of the entire pipeline.…”

Designing a Simple Fiducial Marker for Localization in Spatial Scenes Using Neural Networks

“…STag [ 73 ] is a marker detection system proposed by Benligiray et al in 2019. The focus of the system is for stable and not jittered detection.…”

Droplet: Towards Autonomous Underwater Assembly of Modular Structures

“…The fiducial markers were printed on aluminum with laser cut mounting holes that allow it to be precisely mounted on a white Delrin back plate, fixed onto the aluminum extrusion. We chose STags [8] as fiducial markers, due to their detection robustness underwater in our preliminary experiments compared to other markers commonly used, e.g., ARTag [18]. We printed our blocks in PETG (material commonly used in water bottles) using a Pegasus 12 and a Creality CR-10 3D printer.…”