5 April 2016 Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments
Author Affiliations +
J. of Electronic Imaging, 25(2), 023015 (2016). doi:10.1117/1.JEI.25.2.023015
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.
© 2016 SPIE and IS&T
Haosong Yue, Weihai Chen, Xingming Wu, Jianhua Wang, "Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments," Journal of Electronic Imaging 25(2), 023015 (5 April 2016). https://doi.org/10.1117/1.JEI.25.2.023015




Data modeling

Feature extraction

3D modeling

Error analysis

Back to Top