Dynamic environments localization via dimensions reduction of deep learning features

Hui Zhang; Xiangwei Wang; Xiaoguo Du; Ming Liu; Qijun Chen

doi:10.1007/978-3-319-68345-4_22

Dynamic environments localization via dimensions reduction of deep learning features

Hui Zhang^*, Xiangwei Wang, Xiaoguo Du, Ming Liu, Qijun Chen

^*Corresponding author for this work

Department of Computer Science and Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers
Editors	Markus Vincze, Haoyao Chen, Ming Liu
Publisher	Springer Verlag
Pages	239-253
Number of pages	15
ISBN (Print)	9783319683447
DOIs	https://doi.org/10.1007/978-3-319-68345-4_22
Publication status	Published - 2017
Event	11th International Conference on Computer Vision Systems, ICVS 2017 - Shenzhen, China Duration: 10 Jul 2017 → 13 Jul 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10528 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Computer Vision Systems, ICVS 2017
Country/Territory	China
City	Shenzhen
Period	10/07/17 → 13/07/17

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

Access to Document

10.1007/978-3-319-68345-4_22

Cite this

Zhang, H., Wang, X., Du, X., Liu, M., & Chen, Q. (2017). Dynamic environments localization via dimensions reduction of deep learning features. In M. Vincze, H. Chen, & M. Liu (Eds.), Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers (pp. 239-253). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10528 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68345-4_22

Zhang, Hui ; Wang, Xiangwei ; Du, Xiaoguo et al. / Dynamic environments localization via dimensions reduction of deep learning features. Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers. editor / Markus Vincze ; Haoyao Chen ; Ming Liu. Springer Verlag, 2017. pp. 239-253 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{07a1190c8798414d9dbb3091b6c0c81f,

title = "Dynamic environments localization via dimensions reduction of deep learning features",

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{\textquoteright}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.",

author = "Hui Zhang and Xiangwei Wang and Xiaoguo Du and Ming Liu and Qijun Chen",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 11th International Conference on Computer Vision Systems, ICVS 2017 ; Conference date: 10-07-2017 Through 13-07-2017",

year = "2017",

doi = "10.1007/978-3-319-68345-4\_22",

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isbn = "9783319683447",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Markus Vincze and Haoyao Chen and Ming Liu",

booktitle = "Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers",

}

Zhang, H, Wang, X, Du, X, Liu, M & Chen, Q 2017, Dynamic environments localization via dimensions reduction of deep learning features. in M Vincze, H Chen & M Liu (eds), Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10528 LNCS, Springer Verlag, pp. 239-253, 11th International Conference on Computer Vision Systems, ICVS 2017, Shenzhen, China, 10/07/17. https://doi.org/10.1007/978-3-319-68345-4_22

Dynamic environments localization via dimensions reduction of deep learning features. / Zhang, Hui; Wang, Xiangwei; Du, Xiaoguo et al.
Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers. ed. / Markus Vincze; Haoyao Chen; Ming Liu. Springer Verlag, 2017. p. 239-253 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10528 LNCS).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Dynamic environments localization via dimensions reduction of deep learning features

AU - Zhang, Hui

AU - Wang, Xiangwei

AU - Du, Xiaoguo

AU - Liu, Ming

AU - Chen, Qijun

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N2 - 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.

AB - 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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DO - 10.1007/978-3-319-68345-4_22

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SN - 9783319683447

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers

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Zhang H, Wang X, Du X, Liu M, Chen Q. Dynamic environments localization via dimensions reduction of deep learning features. In Vincze M, Chen H, Liu M, editors, Computer Vision Systems - 11th International Conference, ICVS 2017, Revised Selected Papers. Springer Verlag. 2017. p. 239-253. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-68345-4_22

Dynamic environments localization via dimensions reduction of deep learning features

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