Effective indoor localization is the essential part of VR (Virtual Reality) and AR (Augmented Reality) technologies. Tracking the RGB-D camera becomes more popular since it can capture the relatively accurate color and depth information at the same time. With the recovered colorful point cloud, the traditional ICP (Iterative Closest Point) algorithm can be used to estimate the camera poses and reconstruct the scene. However, many works focus on improving ICP for processing the general scene and ignore the practical significance of effective initialization under the specific conditions, such as the indoor scene for VR or AR. In this work, a novel indoor prior based initialization method has been proposed to estimate the initial motion for ICP algorithm. We introduce the generation process of colorful point cloud at first, and then introduce the camera rotation initialization method for ICP in detail. A fast region growing based method is used to detect planes in an indoor frame. After we merge those small planes and pick up the two biggest unparallel ones in each frame, a novel rotation estimation method can be employed for the adjacent frames. We evaluate the effectiveness of our method by means of qualitative observation of reconstruction result because of the lack of the ground truth. Experimental results show that our method can not only fix the failure cases, but also can reduce the ICP iteration steps significantly.

This paper presents a technique for high-accuracy estimation of image rotation using 1D Phase-Only Correlation (POC). The rotation angle between two images is estimated as follows: (i) compute the amplitude spectra of the given images, (ii) transform the coordinate system of amplitude spectra from Cartesian coordinates to polar coordinates, and (iii) estimate the translational displacement between the polar-mapped amplitude spectra to obtain the rotation angle. While the conventional approach is to employ 2D POC for high-accuracy displacement estimation in (iii), this paper proposes the use of 1D POC with an adaptive line selection scheme. The proposed technique makes possible to improve the accuracy of rotation estimation for low contrast images of artificial objects with regular geometric shapes and to reduce the total computation cost by 50%.