Authors
Jiwu Wang, Shunkai Zheng, Yao Du, Sugisaka Masanori
Corresponding Author
Jiwu Wang
Available Online 1 December 2015.
DOI
https://doi.org/10.2991/jrnal.2015.2.3.11
Keywords
SLAM; ORB; Monocular Vision, Mobile Robot
Abstract
In order to reduce the accumulative errors in our monocular SLAM (simultaneous
localization and mapping), the loop closing (detection + correction) method
based on PTAM (Parallel Tracking and Mapping) is applied. Here natural
environment features are necessary to be extracted efficiently, so the
ORB (Oriented FAST and Rotated BRIEF (Binary Robust Independent Elementary
Features)) algorithm is used for the feature extraction and matching. The
experiment results show that there is strong feature recognition power
in ORB so that it can realize environment recognition under the conditions
of severe viewpoint change. Moreover, it is so fast to extract and match
(without using multi-threading or GPU (Graphics Processing Unit) acceleration)
that it can accurately track and map in real time. Its capability of fast
and efficiency is verified with outdoor scene experiments.
Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).