@InProceedings{10.1007/978-3-319-68345-4_22,
author="Zhang, Hui
and Wang, Xiangwei
and Du, Xiaoguo
and Liu, Ming
and Chen, Qijun",
editor="Liu, Ming
and Chen, Haoyao
and Vincze, Markus",
title="Dynamic Environments Localization via Dimensions Reduction of Deep Learning Features",
booktitle="Computer Vision Systems",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="239--253",
abstract="How to autonomous locate a robot quickly and accurately in dynamic environments is a primary problem for reliable robot navigation. Monocular visual localization combined with deep learning has gained incredible results. However, the features extracted from deep learning are of huge dimensions and the matching algorithm is complex. How to reduce dimensions with precise localization is one of the difficulties. This paper presents a novel approach for robot localization by training in dynamic environments in a large scale. We extracted features from AlexNet and reduced dimensions of features with IPCA, and what's more, we reduced ambiguities with kernel method, normalization and morphology processing to matching matrix. Finally, we detected best matching sequence online in dynamic environments across seasons. Our localization algorithm can locate robots quickly with high accuracy.",
isbn="978-3-319-68345-4"
}