Determining and Improving the Localization Accuracy of AprilTag Detection

Fiducial markers like AprilTags play an important role in robotics, e.g., for the calibration of cameras or the localization of robots. One of the most important properties of an algorithm for detecting such tags is its localization accuracy. In this paper, we present the results of an extensive comparison of four freely available libraries capable of detecting AprilTags, namely AprilTag 3, AprilTags C++, ArUco as standalone libraries, and the OpenCV algorithm based on ArUco. The focus of the comparison is on localization accuracy, but the processing time is also examined. Besides working with pure tags, their extension to checkerboard corners is investigated. In addition, we present two new post-processing techniques. Firstly, a method that can filter out very inaccurate detections resulting from partial border occlusion, and secondly a new highly accurate method for edge refinement. With this we achieve a median pixel error of 0.017 px, compared to 0.17 px for standard OpenCV corner refinement. The dataset used for the evaluation, as well as the developed post-processing techniques, are made publicly available to encourage further comparison and improvement of the detection libraries.