Enhancing AR-to-Robot Registration Accuracy: A Comparative Study of Marker Detection Algorithms and Registration Parameters

Abstract

Augmented Reality (AR) offers potential for enhancing human-robot collaboration by enabling intuitive interaction and real-time feedback. A crucial aspect of AR-robot integration is accurate spatial registration to align virtual content with the physical robotic workspace. This paper systematically investigates the effects of different tracking techniques and registration parameters on AR-to-robot registration accuracy, focusing on paired-point methods. We evaluate four marker detection algorithms - ARToolkit, Vuforia, ArUco, and retroreflective tracking - analyzing the influence of viewing distance, angle, marker size, point distance, distribution, and quantity. Our results show that ARToolkit provides the highest registration accuracy. While larger markers and positioning registration point centroids close to target locations consistently improved accuracy, other factors such as point distance and quantity were highly dependent on the tracking techniques used. Additionally, we propose an effective refinement method using point cloud registration, significantly improving accuracy by integrating data from points recorded between registration locations. These findings offer practical guidelines for enhancing AR-robot registration, with future work needed to assess the transferability to other AR devices and robots.