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International Conference on Computer Vision Systems

ICVS 2017: Computer Vision Systems pp 239–253Cite as

Dynamic Environments Localization via Dimensions Reduction of Deep Learning Features

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10528))

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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Acknowledgment

This research is a cooperation work between RAM-LAB of HKUST and RAI-LAB of Tongji University. Our work is supported by National Natural Science Foundation (61573260), Natural Science Foundation of Shanghai (16JC1401200); Shenzhen Science, Technology and Innovation Commission (SZSTI) (JCYJ20160428154842603 and JCYJ20160401100022706); partially supported by the HKUST Project (IGN16EG12).

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Authors and Affiliations

  1. RAI-LAB, Tongji University, Shanghai, China

    Hui Zhang, Xiangwei Wang, Xiaoguo Du & Qijun Chen

  2. RAM-LAB, Robotics Insititute, HKUST, Hongkong, China

    Ming Liu

Authors
  1. Hui Zhang
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  2. Xiangwei Wang
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  3. Xiaoguo Du
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  4. Ming Liu
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  5. Qijun Chen
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Corresponding author

Correspondence to Hui Zhang .

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Ming Liu

  2. Harbin Institute of Technology, Shenzhen, China

    Haoyao Chen

  3. Technische Universtiät Wien, Vienna, Austria

    Markus Vincze

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Zhang, H., Wang, X., Du, X., Liu, M., Chen, Q. (2017). Dynamic Environments Localization via Dimensions Reduction of Deep Learning Features. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_22

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68344-7

  • Online ISBN: 978-3-319-68345-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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