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Autonomous Robots

, Volume 43, Issue 1, pp 197–211 | Cite as

Topological local-metric framework for mobile robots navigation: a long term perspective

  • Li Tang
  • Yue WangEmail author
  • Xiaqing Ding
  • Huan Yin
  • Rong Xiong
  • Shoudong Huang
Article
  • 377 Downloads

Abstract

Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously.

Keywords

Mobile robot Localization Navigation Lifelong learning 

Notes

Acknowledgements

This work was supported by the National Nature Science Foundation of China (Grant Nos. U1609210, 61473258 and 61621002), National Key Research and Development Program (Grant No. 2017YFB1300400), and in part by the Joint Centre for Robotics Research between Zhejiang University and the University of Technology, Sydney.

Supplementary material

10514_2018_9724_MOESM1_ESM.mp4 (26.8 mb)
Supplementary material 1 (mp4 27464 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Li Tang
    • 1
  • Yue Wang
    • 1
    • 2
    Email author
  • Xiaqing Ding
    • 1
  • Huan Yin
    • 1
  • Rong Xiong
    • 1
  • Shoudong Huang
    • 3
  1. 1.State Key Laboratory of Industrial Control and Technology, and Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouPeople’s Republic of China
  2. 2.iPlusBotHangzhouPeople’s Republic of China
  3. 3.Center for Autonomous Systems (CAS)University of Technology SydneySydneyAustralia

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