Augmented reality (AR) systems support complex tasks like assembly by overlaying task-related content onto the real world. In recent years, the effort of designing and developing assembly task support systems in AR decreased with the availability of high potential head-mounted displays and provision of integrated development environments. Nevertheless, problems still arise when companies craft an effective AR task support system, particularly in the difficulty of selecting appropriate techniques and information-presentation methods, and the requirements that vary with each use case. In this study, we formulated a corresponding guideline, developed a selection aid tool that incorporates filtering based on the categorization of subtasks and the degree of freedom of available tracking, and evaluated their effectiveness in two experiments. First, to confirm effects on system design, we asked 18 participants to perform the design action of the AR system with the guideline for two tasks (PC assembly and rope work). Consequently, to verify the quality of the designed AR systems from Experiment 1, we asked another set of 20 participants to perform the same tasks with those systems. The results confirm that using the guideline can considerably lower efforts creating media and alleviate the error for a specific process. We envision our guideline and tool to be accessible as an online web page, assisting AR assembly task support system designer/developers worldwide.

Inspection tasks focus on observation of the environment and are required in many industrial domains. Inspectors usually execute these tasks by using a guide such as a paper manual, and directly observing the environment. The effort required to match the information in a guide with the information in an environment and the constant gaze shifts required between the two can severely lower the work efficiency of inspector in performing his/her tasks. Augmented reality (AR) allows the information in a guide to be overlaid directly on an environment. This can decrease the amount of effort required for information matching, thus increasing work efficiency. AR guides on head-mounted displays (HMDs) have been shown to increase efficiency. Handheld AR (HAR) is not as efficient as HMD-AR in terms of manipulability, but is more practical and features better information input and sharing capabilities. In this study, we compared two handheld guides: an AR interface that shows 3D registered annotations, that is, annotations having a fixed 3D position in the AR environment, and a non-AR picture interface that displays non-registered annotations on static images. We focused on inspection tasks that involve high information density and require the user to move, as well as to perform several viewpoint alignments. The results of our comparative evaluation showed that use of the AR interface resulted in lower task completion times, fewer errors, fewer gaze shifts, and a lower subjective workload. We are the first to present findings of a comparative study of an HAR and a picture interface when used in tasks that require the user to move and execute viewpoint alignments, focusing only on direct observation. Our findings can be useful for AR practitioners and psychology researchers.

In this paper, we propose a relocalization method for keyframe-based SLAM that enables real-time and accurate recovery from tracking failures. To realize an AR-based application in a real world situation, not only accurate camera tracking but also fast and accurate relocalization from tracking failure is required. The previous keyframe-based relocalization methods have some drawbacks with regard to speed and accuracy. The proposed relocalization method selects two algorithms adaptively depending on the relative camera pose between a current frame and a target keyframe. In addition, it estimates a degree of false matches to speed up RANSAC-based model estimation. We present effectiveness of our method by an evaluation using public tracking dataset.