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Journal of Electronic Imaging

Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments
Author(s): Haosong Yue; Weihai Chen; Xingming Wu; Jianhua Wang
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Paper Abstract

Three-dimensional (3-D) simultaneous localization and mapping (SLAM) is a crucial technique for intelligent robots to navigate autonomously and execute complex tasks. It can also be applied to shape measurement, reverse engineering, and many other scientific or engineering fields. A widespread SLAM algorithm, named KinectFusion, performs well in environments with complex shapes. However, it cannot handle translation uncertainties well in highly structured scenes. This paper improves the KinectFusion algorithm and makes it competent in both structured and unstructured environments. 3-D line features are first extracted according to both color and depth data captured by Kinect sensor. Then the lines in the current data frame are matched with the lines extracted from the entire constructed world model. Finally, we fuse the distance errors of these line-pairs into the standard KinectFusion framework and estimate sensor poses using an iterative closest point-based algorithm. Comparative experiments with the KinectFusion algorithm and one state-of-the-art method in a corridor scene have been done. The experimental results demonstrate that after our improvement, the KinectFusion algorithm can also be applied to structured environments and has higher accuracy. Experiments on two open access datasets further validated our improvements.

Paper Details

Date Published: 5 April 2016
PDF: 12 pages
J. Electron. Imag. 25(2) 023015 doi: 10.1117/1.JEI.25.2.023015
Published in: Journal of Electronic Imaging Volume 25, Issue 2
Show Author Affiliations
Haosong Yue, BeiHang Univ. (China)
Weihai Chen, BeiHang Univ. (China)
Xingming Wu, BeiHang Univ. (China)
Jianhua Wang, BeiHang Univ. (China)

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