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Abstract Augmented reality (AR) enhances the user’s perception of the real environment by superimposing virtual images generated by computers. These virtual images provide additional visual information that complements the real-world view. AR systems are rapidly gaining popularity in various manufacturing fields such as training, maintenance, assembly, and robot programming. In some AR applications, it is crucial for the invisible virtual environment to be precisely aligned with the physical environment to ensure that human users can accurately perceive the virtual augmentation in conjunction with their real surroundings. The process of achieving this accurate alignment is known as calibration. During some robotics applications using AR, we observed instances of misalignment in the visual representation within the designated workspace. This misalignment can potentially impact the accuracy of the robot’s operations during the task. Based on the previous research on AR-assisted robot programming systems, this work investigates the sources of misalignment errors and presents a simple and efficient calibration procedure to reduce the misalignment accuracy in general video see-through AR systems. To accurately superimpose virtual information onto the real environment, it is necessary to identify the sources and propagation of errors. In this work, we outline the linear transformation and projection of each point from the virtual world space to the virtual screen coordinates. An offline calibration method is introduced to determine the offset matrix from the head-mounted display (HMD) to the camera, and experiments are conducted to validate the improvement achieved through the calibration process. 

Abstract This paper aims to present a potential cybersecurity risk existing in mixed reality (MR)-based smart manufacturing applications that decipher digital passwords through a single RGB camera to capture the user’s mid-air gestures. We first created a test bed, which is an MR-based smart factory management system consisting of mid-air gesture-based user interfaces (UIs) on a video see-through MR head-mounted display. To interact with UIs and input information, the user’s hand movements and gestures are tracked by the MR system. We setup the experiment to be the estimation of the password input by users through mid-air hand gestures on a virtual numeric keypad. To achieve this goal, we developed a lightweight machine learning-based hand position tracking and gesture recognition method. This method takes either video streaming or recorded video clips (taken by a single RGB camera in front of the user) as input, where the videos record the users’ hand movements and gestures but not the virtual UIs. With the assumption of the known size, position, and layout of the keypad, the machine learning method estimates the password through hand gesture recognition and finger position detection. The evaluation result indicates the effectiveness of the proposed method, with a high accuracy of 97.03%, 94.06%, and 83.83% for 2-digit, 4-digit, and 6-digit passwords, respectively, using real-time video streaming as input with known length condition. Under the unknown length condition, the proposed method reaches 85.50%, 76.15%, and 77.89% accuracy for 2-digit, 4-digit, and 6-digit passwords, respectively.