We propose a hybrid camera pose estimation method using an inclination sensor value and correspondence-free line segments. In this method, possible azimuths of the camera pose are hypothesized by a voting method under an inclination constraint. Then some camera positions for each possible azimuth are calculated based on the detected line segments that affirmatively voted for the azimuth. Finally, the most consistent one is selected as the camera pose out of the multiple sets of the camera positions and azimuths. Unlike many other tracking methods, our method does not use past information but rather estimates the camera pose using only present information. This feature is useful for an initialization measure of registration in augmented reality (AR) systems. This paper describes the details of the method and shows its effectiveness with experiments in which the method is actually used in an AR application.

This work describes a new hybrid vision-based registration method with an inclination sensor. In the method, a camera is tracked by solving linear equations under inclination constraint. Linear operations are faster than the nonlinear optimization process, but the output does not usually satisfy the orthonormality constraint. On the contrary, the proposed method calculates the camera position and azimuth directly, thus the result satisfies the orthonormality constraint. Many hybrid approaches using inertia sensors have been proposed for AR/MR. However, such methods still depend on vision-based methods in initialization processes. On the other hand, the proposed method is totally hybrid in that the inclination measured by the sensor is always incorporated in pose calculation process as well as vision information. The method can be used in initialization process of conventional hybrid methods as well as it can be used as an independent registration method. The proposed method can be applied to not only inside-out-style camera tracking but also outside-in-style object tracking.

This paper describes a calibration method of markers which are used for registration in MR applications. There have been many vision-based approaches proposed as registration methods in MR. When multiple markers are utilized in a vision-based method, it is necessary that the geometric information of the marker arrangement such as their positions and orientations be known in advance. In this paper, we propose a hybrid method combining the "bundle adjustment method," which is a photogrammetric technique that calculates the geometric information from a set of images, with some constraints on the marker arrangement which are obtained a priori (e.g. the multiple markers are located on a single plane). After considering marker arrangements seen in many MR systems, we summarize some constraints seen in these arrangements. Then, we explain the basic framework of this method as well as a solution method under some practical constraints. We further describe several experiments and their results in order to show the effectiveness of this method.