High order visual words for structure-aware and viewpoint-invariant loop closure detection
Loukas Bampis, Angelos Amanatiadis, Antonios Gasteratos
Abstract
In the field of loop closure detection, the most conventional approach is based on the Bag-of-Visual-Words (BoVW) image representation. Although well-established, this model rejects the spatial information regarding the local feature points' layout and performs the associations based only on their similarities. In this paper we propose a novel BoVW-based technique which additionally incorporates the operational environment's structure into the description, treating bunches of visual words with similar optical flow measurements as single similarity votes. The presented experimental results prove that our method offers superior loop closure detection accuracy while still ensuring real-time performance, even in the case of a low power consuming mobile device.
BibTeX
@inproceedings{iros2017_highordervisualw,
title = {High order visual words for structure-aware and viewpoint-invariant loop closure detection},
author = {Loukas Bampis and Angelos Amanatiadis and Antonios Gasteratos},
booktitle = {IROS 2017},
year = {2017}
}