@inproceedings{15c6599425444c65b7280ace147580f8,
title = "Place recognition based on surface graph for a mobile robot",
abstract = "Place recognition is widely used in the loop closure detection in SLAM. The current approach to place recognition is based on RGB images, but there are relatively few place recognition studies using a point cloud. This study presents the place recognition method based on the surface graph. The proposed method clusters the surfaces in the point cloud and recognizes a place through a surface descriptor and a surface graph. The advantage of this approach is that it uses the surfaces that are not low-level features such as SIFT and SURF. Another advantage is that the proposed place recognition is robust because of the surface graph. We have experimented on the data set obtained by the mobile robot equipped with a Kinect sensor in the indoor environment. The experimental results show that the proposed place recognition based on the surface graph (PRSG) scheme is useful and can be used as a loop closure detector.",
keywords = "Fast point feature histogram, Place recognition, Surface graph",
author = "Hyejun Yu and Chae, {Hee Won} and Song, {Jae Bok}",
note = "Funding Information: This research was supported by the MOTIE under the Industrial Foundation Technology Development Program supervised by the KEIT (No. 10051155) Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017 ; Conference date: 28-06-2017 Through 01-07-2017",
year = "2017",
month = jul,
day = "25",
doi = "10.1109/URAI.2017.7992746",
language = "English",
series = "2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "342--346",
booktitle = "2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017",
}