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Uncalibrated downward-looking UAV visual compass based on clustered point features

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References

  1. Wang K L, Ke Y J, Chen B M. Autonomous reconfigurable hybrid tail-sitter UAV U-Lion. Sci China Inf Sci, 2017, 60: 033201

    Article  Google Scholar 

  2. Peng K M, Lin F, Chen B M. Online schedule for autonomy of multiple unmanned aerial vehicles. Sci China Inf Sci, 2017, 60: 072203

    Article  Google Scholar 

  3. Li P, Yu X, Peng X Y, et al. Fault-tolerant cooperative control for multiple UAVs based on sliding mode techniques. Sci China Inf Sci, 2017, 60: 070204

    Article  Google Scholar 

  4. Mariottini G L, Scheggi S, Morbidi F, et al. An accurate and robust visual-compass algorithm for robot-mounted omnidirectional cameras. Robot Auton Syst, 2012, 60: 1179–1190

    Article  Google Scholar 

  5. Bermudez-Cameo J, Puig L, Guerrero J J. Hypercata-dioptric line images for 3D orientation and image rectification. Robot Auton Syst, 2012, 60: 755–768

    Article  Google Scholar 

  6. Labrosse F. The visual compass: performance and limitations of an appearance-based method. J Field Robot, 2006, 23: 913–941

    Article  Google Scholar 

  7. Sturm J, Visser A. An appearance-based visual compass for mobile robots. Robot Auton Syst, 2009, 57: 536–545

    Article  Google Scholar 

  8. Montiel J M M, Davison A J. A visual compass based on SLAM. In: Proceedings of IEEE International Conference on Robotics and Automation, 2006. 1917–1922

  9. Sabnis A, Vachhani L. A multiple camera based visual compass for a mobile robot in dynamic environment. In: Proceedings of IEEE International Conference on Control Applications, 2013. 611–616

  10. Ester M. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996. 226–231

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61673341, 61573091, 61622308, 61873206), National Key R&D Program of China (Grant No. 2016YFD0200701-3), and Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (Grant No. ICT1800421).

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Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China

    Ying Liu, Yu Zhang & Ping Li

  2. School of Automation, Northwestern Polytechnical University, Xi’an, 710072, China

    Bin Xu

Authors
  1. Ying Liu
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  2. Yu Zhang
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  3. Ping Li
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  4. Bin Xu
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Correspondence to Yu Zhang.

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Liu, Y., Zhang, Y., Li, P. et al. Uncalibrated downward-looking UAV visual compass based on clustered point features. Sci. China Inf. Sci. 62, 199202 (2019). https://doi.org/10.1007/s11432-018-9748-1

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  • DOI: https://doi.org/10.1007/s11432-018-9748-1

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