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Erschienen in:
Towards a Cloud Robotics Platform for Distributed Visual SLAM Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Dynamic Environments Localization via Dimensions Reduction of Deep Learning Features

verfasst von : Hui Zhang, Xiangwei Wang, Xiaoguo Du, Ming Liu, Qijun Chen

Erschienen in: Computer Vision Systems

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Edge Detection Using Convolutional Neural Networks for Nematode Development and Adaptation Analysis
Nächstes Kapitel A Gesture Recognition Method Based on Binocular Vision System
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Metadaten
Titel
Dynamic Environments Localization via Dimensions Reduction of Deep Learning Features
verfasst von
Hui Zhang
Xiangwei Wang
Xiaoguo Du
Ming Liu
Qijun Chen
Copyright-Jahr
2017
Buch
Computer Vision Systems

Print ISBN: 978-3-319-68344-7

Electronic ISBN: 978-3-319-68345-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-68345-4_22