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SCIENCE CHINA Information Sciences, Volume 61, Issue 7: 079201(2018) https://doi.org/10.1007/s11432-017-9336-x

Multi-robot coordinated exploration of indoor environments using semantic information

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  • ReceivedAug 8, 2017
  • AcceptedNov 30, 2017
  • PublishedJun 13, 2018

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61633002).


Supplement

Appendix A–E.


References

[1] Stachniss C. Robotic Mapping and Exploration. Berlin: Springer, 2009. Google Scholar

[2] Stachniss C, Mozos \'{O} M, Burgard W. Efficient exploration of unknown indoor environments using a team of mobile robots. Ann Math Artif Intel, 2008, 52: 205-227 CrossRef Google Scholar

[3] Fang H, Lu S L, Chen J. New advances in complex motion control for single robot systems and multi-agent systems. Sci China Tech Sci, 2016, 59: 1963-1964 CrossRef Google Scholar

[4] Burgard W, Moors M, Stachniss C. Coordinated multi-robot exploration. IEEE Trans Robot, 2005, 21: 376-386 CrossRef Google Scholar

[5] Obwald S, Bennewitz M, Burgard W. Speeding-up robot exploration by exploiting background information. IEEE Robot Autom Lett, 2016, 1: 716-723 CrossRef Google Scholar

[6] Liu P, Zhou D, Wu N J. VDBSCAN: varied density based spatial clustering of applications with noise. In: Proceedings of the International Conference on Service Systems and Service Management, Hong Kong, 2007. Google Scholar

[7] Rottmann A, Mozos Ó M, Stachniss C, et al. Semantic place classification of indoor environments with mobile robots using boosting. In: Proceedings of the 20th National Conference on Artificial Intelligence, Pittsburgh, 2005. 1306--1311. Google Scholar

  • Figure 1

    (Color online) Illustration of the architecture of our multi-robot exploration system. (a) Typical indoor environment with different semantic classifications; (b) Kinect and laser range scanner; (c) CNN-based classifier for semantic classification of indoor places; (d) candidate target frontiers decision-making process with VDBSCAN; (e) hidden Markov model for estimating the semantic classification; (f) our proposed target frontier assignment strategy; (g) the generated grid-map.

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