Position Interpolation Using Feature Point Scale for Decimeter Visual Localization
Abstract
Vehicle ego-localization is a critical task not only for in-car navigation systems, but also for emerging intelligent and autonomous vehicle technologies. Visual localization methods that determine current location by performing image matching against a pre-constructed database have an accuracy limited by the spatial distance between database images. In this paper we propose a method that uses the scale of feature points to interpolate the position of the query image between two database images. We show how this simple contribution offers an appreciable improvement in localization accuracy with an extremely minimal increase in processing time, especially when used in conjunction with image matching methods that already monitor feature scale. Our experiments showed an increase of up to 33% in average localization accuracy when compared to a method without any interpolation.
Cite
Text
Wong et al. "Position Interpolation Using Feature Point Scale for Decimeter Visual Localization." IEEE/CVF International Conference on Computer Vision Workshops, 2015. doi:10.1109/ICCVW.2015.22Markdown
[Wong et al. "Position Interpolation Using Feature Point Scale for Decimeter Visual Localization." IEEE/CVF International Conference on Computer Vision Workshops, 2015.](https://mlanthology.org/iccvw/2015/wong2015iccvw-position/) doi:10.1109/ICCVW.2015.22BibTeX
@inproceedings{wong2015iccvw-position,
title = {{Position Interpolation Using Feature Point Scale for Decimeter Visual Localization}},
author = {Wong, David and Deguchi, Daisuke and Ide, Ichiro and Murase, Hiroshi},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2015},
pages = {90-97},
doi = {10.1109/ICCVW.2015.22},
url = {https://mlanthology.org/iccvw/2015/wong2015iccvw-position/}
}