Effective Indoor Localization and 3D Point Registration Based on Plane Matching Initialization

Dongchen ZHU  Ziran XING  Jiamao LI  Yuzhang GU  Xiaolin ZHANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.6   pp.1316-1324
Publication Date: 2017/06/01
Publicized: 2017/03/08
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDP7379
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
Keyword: 
ICP (Iterative Closest Point),  indoor localization,  indoor reconstruction,  plane matching,  rotation estimation,  

Full Text: PDF(2.2MB)>>
Buy this Article



Summary: 
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.