전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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드론 자율비행 기술 동향

Survey on Developing Autonomous Micro Aerial Vehicles

  • 발행 : 2021.04.01
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초록

As sensors such as Inertial Measurement Unit, cameras, and Light Detection and Rangings have become cheaper and smaller, research has been actively conducted to implement functions automating micro aerial vehicles such as multirotor type drones. This would fully enable the autonomous flight of drones in the real world without human intervention. In this article, we present a survey of state-of-the-art development on autonomous drones. To build an autonomous drone, the essential components can be classified into pose estimation, environmental perception, and obstacle-free trajectory generation. To describe the trend, we selected three leading research groups-University of Pennsylvania, ETH Zurich, and Carnegie Mellon University-which have demonstrated impressive experiment results on automating drones using their estimation, perception, and trajectory generation techniques. For each group, we summarize the core of their algorithm and describe how they implemented those in such small-sized drones. Finally, we present our up to date research status on developing an autonomous drone.

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참고문헌

  1. K. Sun et al., "Robust stereo visual inertial odometry for fast autonomous flight," IEEE Robot. Autom. Lett. vol. 3, no. 2, 2018, pp. 965-972. https://doi.org/10.1109/lra.2018.2793349
  2. S. S. Shivakumar et al., "Real time dense depth estimation by fusing stereo with sparse depth measurements," in Proc. Int. Conf. Robot. Autom. (Montreal, Canada), May 2019.
  3. S. S. Liu et al., "Search-based motion planning for quadrotors using linear quadratic minimum time control," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (Vancouver, Canada), Sept. 2017.
  4. M. Quigley et al., "The open vision computer: An integrated sensing and compute system for mobile robots," in Proc. Int. Conf. Robot. Autom. (Montreal, Canada), May 2019.
  5. https://www.youtube.com/watch?v=vDYy3L9nvLk&ab_channel=DARPAtv
  6. M. Bloesch et al., "Robust visual inertial odometry using a direct EKF-based approach," IEEE/RSJ Int. Conf. Intell. Robot. Syst. (Hamburg, Germany), Oct. 2015.
  7. https://github.com/ethz-asl/maplab/wiki/ROVIOLIIntroduction
  8. T. Schneider et al., "Maplab: An open framework for research in visual-inertial mapping and localization," IEEE Robot. Autom. Lett. vol. 3, no. 3, 2018, 1418-1425. https://doi.org/10.1109/lra.2018.2800113
  9. H. Oleynikova, "Mapping and planning for safe collision avoidance on-board micro-aerial vehicles," Doctoral Thesis, ETH Zurich, 2019.
  10. M. Zucker et al., "Chomp: Covariant hamiltonian optimization for motion planning," The Int. J. Robot. Res. vol. 32, no. 9-10, 2013, pp. 1164-1193. https://doi.org/10.1177/0278364913488805
  11. H. Oleynikova et al., "Continuous-time trajectory optimization for online UAV replanning," in Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst. (Daejeon, Rep. of Korea), Oct. 2016.
  12. J. Nikolic et al., "A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM," in Proc. IEEE Int. Conf. Robot. Autom. (Hong Kong, China), May 2014.
  13. H. Oleynikova et al., "An open-source system for vision-based micro-aerial vehicle mapping, planning, and flight in cluttered environments," J. Field Robot. vol. 37, no. 4, 2020, pp. 642-666. https://doi.org/10.1002/rob.21950
  14. J. Zhang and S. Sanjiv, "Laser-visual-inertial odometry and mapping with high robustness and low drift," J. Field Robot. vol. 35, no. 8, 2018, pp. 1242-1264. https://doi.org/10.1002/rob.21809
  15. J. Zhang and S. Sanjiv, "LOAM: Lidar odometry and mapping in real-time," Robot.: Sci. Syst. vol. 2, no. 9, 2014.
  16. J. Zhang et al., "Maximum likelihood path planning for fast aerial maneuvers and collision avoidance," in Proc. IEEE/RSJ Int. Conf. Intell. Robots. Syst. (Macau, China), Nov. 2019.