We have been developing a mixed reality system to support road maintenance using overlaid visual aids. Such a system requires a positioning method that can provide sub-meter accuracy and function even if the appearance of the road surface changes significantly caused by many factors such as construction phase, time and weather. Therefore, we are developing a real-time worker positioning method that can be applied to these situation by integrating laser range finder (LRF) and pedestrian dead-reckoning (PDR) data. In the field, multiple workers move around the workspace. Therefore, it is necessary to determine corresponding pairs of PDR-based and LRF-based trajectories by identifying similar trajectories. In this study, we propose a method to calculate the similarity between trajectories and a procedure to integrate corresponding pairs of trajectories to acquire the position and movement direction of a worker.

This paper presents a study of a method for estimating the position and orientation of a photo-shoot in indoor environments for augmented reality applications. Our proposed localization method is based on registered frame data of virtualized reality models, which are photos with known photo-shoot positions and orientations, and depth data. Because registered frame data are secondary product of modeling process, additional works are not necessary to create registered frame data especially for the localization. In the method, a photo taken by a mobile camera is compared to registered frame data for the localization. Since registered frame data are linked with photo-shoot position, orientation, and depth data, 3D coordinates of each pixel on the photo of registered frame data is available. We conducted experiments with employing five techniques of the estimation for comparative evaluations.

In annotation overlay applications using augmented reality (AR), view management is widely used for improving readability and intelligibility of the annotations. In order to recognize the visible portions of objects in the user's view, the positions, orientations, and shapes of the objects should be known in the case of conventional view management methods. However, it is difficult for a wearable AR system to obtain the positions, orientations and shapes of objects because the target object is usually moving or non-rigid. In this paper, we propose a view management method to overlay annotations of moving or non-rigid objects for networked wearable AR. The proposed method obtains positions and shapes of target objects via a network in order to estimate the visible portions of the target objects in the user's view. Annotations are located by minimizing penalties related to the overlap of an annotation, occlusion of target objects, length of a line between the annotation and the target object, and distance of the annotation in sequential frames. Through experiments, we have proven that the prototype system can correctly provide each user with annotations on multiple users of wearable AR systems.