Qualitative Image Based Localization in Indoors Environments
Abstract
Man made indoor environments possess regularities, which can be efficiently exploited in automated model acquisition by means of visual sensing. In this context we propose an approach for inferring a topological model of an environment from images or the video stream captured by a mobile robot during exploration. The proposed model consists of a set of locations and neighborhood relationships between them. Initially each location in the model is represented by a collection of similar, temporally adjacent views, with the similarity defined according to a simple appearance based distance measure. The sparser representation is obtained in a subsequent learning stage by means of learning vector quantization (LVQ). The quality of the model is tested in the context of qualitative localization scheme by means of location recognition: given a new view, the most likely location where that view came from is determined.
Cite
Text
Kosecká et al. "Qualitative Image Based Localization in Indoors Environments." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003. doi:10.1109/CVPR.2003.1211445Markdown
[Kosecká et al. "Qualitative Image Based Localization in Indoors Environments." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003.](https://mlanthology.org/cvpr/2003/kosecka2003cvpr-qualitative/) doi:10.1109/CVPR.2003.1211445BibTeX
@inproceedings{kosecka2003cvpr-qualitative,
title = {{Qualitative Image Based Localization in Indoors Environments}},
author = {Kosecká, Jana and Zhou, Liang and Barber, Philip and Duric, Zoran},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2003},
pages = {3-10},
doi = {10.1109/CVPR.2003.1211445},
url = {https://mlanthology.org/cvpr/2003/kosecka2003cvpr-qualitative/}
}