Journal
JOURNAL OF ELECTRONIC IMAGING
Volume 25, Issue 2, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JEI.25.2.023015
Keywords
simultaneous localization and mapping; structured environments; three-dimensional line features; iterative closest point
Funding
- National Natural Science Foundation of China [61175108, 51475017]
Ask authors/readers for more resources
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. (C) 2016 SPIE and IS&T
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available