An Automatic Marker–Object Offset Calibration Method for Precise 3D Augmented Reality Registration in Industrial Applications

Abstract

Industrial augmented reality (AR) applications demand high on the visual consistency of virtual-real registration. To present, the marker-based registration method is most popular because it is fast, robust, and convenient to obtain the registration matrix. In practice, the registration matrix should multiply an offset matrix that describes the transformation between the attaching position and the initial position of the marker relative to the object. However, the offset matrix is usually measured, calculated, and set manually, which is not accurate and convenient. This paper proposes an accurate and automatic marker−object offset matrix calibration method. First, the normal direction of the target object is obtained by searching and matching the top surface of the CAD model. Then, the spatial translation is estimated by aligning the projected and the imaged top surface. Finally, all six parameters of the offset matrix are iteratively optimized using a 3D image alignment framework. Experiments were performed on the publicity monocular rigid 3D tracking dataset and an automobile gearbox. The average translation and rotation errors of the optimized offset matrix are 2.10 mm and 1.56 degree respectively. The results validate that the proposed method is accurate and automatic, which contributes to a universal offset matrix calibration tool for marker-based industrial AR applications.

Keywords

• augmented reality
• ar registration
• offset calibration
• industrial ar
• pose estimation
• image descriptor
• image alignment
• cad model