IcoTag3D: Enhanced 6-DoF Pose Estimation for Robotic Arms Using TriangleTag Markers on an Icosahedron

Abstract

High-precision pose estimation using fiducial markers has many applications in medical device tracking, virtual reality alignment, navigation, and more. However, the accuracy of pose estimation and detection capabilities are often constrained by the shape and scale of the fiducial marker plane. In this article, we propose a triangular planar fiducial marker affixed to a positive icosahedron for pose estimation. This design expands the angular observation range, increases the marker scale, and consequently enhances estimation accuracy and recognition distance. The 2-D coordinates of the feature points from the markers are detected and extracted from the environment. Subsequently, the 3-D coordinates of these feature points are obtained using the triangulation method. This process results in the formation of 2-D–3-D point pairs. High-quality interior points are then filtered using the random sample consensus (RANSAC) method. The initial position is determined through the efficient perspective-n-point (EPnP) method, followed by the application of Levenberg–Marquardt (LM) optimization. We evaluated the performance of IcoTag3D through both simulations and physical experiments. The results from the simulation experiments indicate that IcoTag3D exhibits significantly lower maximum rotation angle error, reprojection error, and translation error at the submillimeter level. In addition, it demonstrates an improved recognition distance compared with the method of attaching ArUco markers to icosahedra. Physical experiments have further confirmed the feasibility of IcoTag3D.