Dynamic Environments Localization via Dimensions Reduction of Deep Learning Features

  • Hui ZhangEmail author
  • Xiangwei Wang
  • Xiaoguo Du
  • Ming Liu
  • Qijun Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10528)


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.


Deep Learning Features AlexNet Matching Matrix Hand-crafted Feature Descriptors Large Scale Visual Recognition Challenge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hui Zhang
    • 1
    Email author
  • Xiangwei Wang
    • 1
  • Xiaoguo Du
    • 1
  • Ming Liu
    • 2
  • Qijun Chen
    • 1
  1. 1.RAI-LABTongji UniversityShanghaiChina
  2. 2.RAM-LAB, Robotics InsitituteHKUSTHongkongChina

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