Title
A Post-Rectification Approach of Depth Images of Kinect v2 for 3D Reconstruction of Indoor Scenes.
Abstract
3D reconstruction of indoor scenes is a hot research topic in computer vision. Reconstructing fast, low-cost, and accurate dense 3D maps of indoor scenes have applications in indoor robot positioning, navigation, and semantic mapping. In other studies, the Microsoft Kinect for Windows v2 (Kinect v2) is utilized to complete this task, however, the accuracy and precision of depth information and the accuracy of correspondence between the RGB and depth (RGB-D) images still remain to be improved. In this paper, we propose a post-rectification approach of the depth images to improve the accuracy and precision of depth information. Firstly, we calibrate the Kinect v2 with a planar checkerboard pattern. Secondly, we propose a post-rectification approach of the depth images according to the reflectivity-related depth error. Finally, we conduct tests to evaluate this post-rectification approach from the perspectives of accuracy and precision. In order to validate the effect of our post-rectification approach, we apply it to RGB-D simultaneous localization and mapping (SLAM) in an indoor environment. Experimental results show that once our post-rectification approach is employed, the RGB-D SLAM system can perform a more accurate and better visual effect 3D reconstruction of indoor scenes than other state-of-the-art methods.
Year
DOI
Venue
2017
10.3390/ijgi6110349
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
Keywords
Field
DocType
camera calibration,Kinect v2,reflectivity-related depth error,simultaneous localization and mapping (SLAM),time-of-flight
Computer vision,Semantic mapping,Camera resectioning,RGB color model,Artificial intelligence,Accuracy and precision,Robot,Simultaneous localization and mapping,Geography,Calibration,3D reconstruction
Journal
Volume
Issue
ISSN
6
11
2220-9964
Citations 
PageRank 
References 
1
0.36
6
Authors
5
Name
Order
Citations
PageRank
Jichao Jiao1186.53
Libin Yuan210.70
Weihua Tang321.11
Zhongliang Deng4217.47
Qi Wu511.03