전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제54권3호
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  • Pages.108-118
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  • 2017
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  • 2287-5026(pISSN)
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  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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소나 기반 수중 로봇의 실시간 위치 추정 및 지도 작성에 대한 실험적 검증

Experimental result of Real-time Sonar-based SLAM for underwater robot

  • 이영준 (한국해양과학기술원 부설 선박해양플랜트연구소 수중로봇연구실) ;
  • 최진우 (한국해양과학기술원 부설 선박해양플랜트연구소 수중로봇연구실) ;
  • 고낙용 (조선대학교 전자정보공과대학 제어계측로봇공학과) ;
  • 김태진 (한국해양과학기술원 부설 선박해양플랜트연구소 수중로봇연구실) ;
  • 최현택 (한국해양과학기술원 부설 선박해양플랜트연구소 수중로봇연구실)
  • Lee, Yeongjun (Marine robotics Lab., Korea Research Institute Of Ships & Ocean engineering) ;
  • Choi, Jinwoo (Marine robotics Lab., Korea Research Institute Of Ships & Ocean engineering) ;
  • Ko, Nak Yong (Dept. Control, Instrumentation and Robot Engineering, Chosun University) ;
  • Kim, Taejin (Marine robotics Lab., Korea Research Institute Of Ships & Ocean engineering) ;
  • Choi, Hyun-Taek (Marine robotics Lab., Korea Research Institute Of Ships & Ocean engineering)
  • 투고 : 2016.08.08
  • 심사 : 2017.02.15
  • 발행 : 2017.03.25
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초록

본 논문은 수중 로봇 항법에 사용하기 위한 영상 소나 기반 SLAM (simultaneous localization and mapping) 방법을 제안하고, 성능 평가를 위해 실제 로봇에 탑재하여 실험한 내용을 소개한다. 일반적인 수중 항법은 관성 센서에서 출력되는 정보를 바탕으로 로봇의 위치 및 자세(x,y,z,${\phi}$,${\theta}$,${\psi}$)를 추정한다. 하지만, 장시간 주행할 경우 위치 오차의 누적으로 인하여 정확도가 감소하게 된다. 이에 본 논문에서는 영상 소나로부터 얻을 수 있는 외부 정보를 바탕으로 관성 항법의 위치 추정 성능을 높이고 지도 작성을 수행할 수 있는 SLAM 방법을 제안하고자 한다. 영상 소나를 위한 인공 표식물과 확률 기반 물체 인식 구조를 통해 인공 표식물의 인식 성능을 높이고, 이를 통해 얻게 된 인공 표식물의 위치 정보를 활용하여 관성 항법의 누적 오차를 줄이고자 한다. 항법 알고리즘으로는 확장형 칼만 필터(Extended Kalman Filter, EKF)를 적용하여 로봇의 위치 및 자세를 추정하고 지도를 작성한다. 제안한 방법은 선박해양플랜트연구소에서 보유 중인 수중 로봇 'yShark'에 탑재하여 대형 수조에서 실시간 검증을 수행하였다.

This paper presents experimental results of realtime sonar-based SLAM (simultaneous localization and mapping) using probability-based landmark-recognition. The sonar-based SLAM is used for navigation of underwater robot. Inertial sensor as IMU (Inertial Measurement Unit) and DVL (Doppler Velocity Log) and external information from sonar image processing are fused by Extended Kalman Filter (EKF) technique to get the navigation information. The vehicle location is estimated by inertial sensor data, and it is corrected by sonar data which provides relative position between the vehicle and the landmark on the bottom of the basin. For the verification of the proposed method, the experiments were performed in a basin environment using an underwater robot, yShark.

키워드

참고문헌

  1. B. Kalyan, A. Balasuriya and S. Wijesoma, "Multiple target tracking in Underwater Sonar Images using Particle-PHD filter," International Conference on OCEANS, 2006.
  2. M. Pinto, B. Ferreira, A. Matos and N. Cruz, "Using side scan sonar to relative navigation," International Conference on OCEANS, 2009.
  3. A. Mallios, P. Ridao, D. Ribas, F. Maurelli and Y. Petillot, "EKF-SLAM for AUV navigation under probabilistic sonar scan-matching," International Conference on Intelligent Robots and Systems (IROS), pp. 4404-4411, 2010.
  4. TG. Kim, and NY. Ko, "Localization of an Underwater Robot using Acoustic Signal," Journal of Korea Robotics Society, vol. 7, no. 4, pp. 231-242, 2012. https://doi.org/10.7746/jkros.2012.7.4.231
  5. D. Lee, D. Kim, S. Lee, H. Myung and H. T. Choi, "Experiments on localization of an AUV using graph-based SLAM," International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 526-527, 2013.
  6. H. Johannsson, M. Kaess, B. Englot, F. Hover, and J. Leonard, "Imaging sonar-aided navigation for autonomous underwater harbor surveillance," International Conference on Intelligent Robots and Systems (IROS), pp. 4396-4403, 2010.
  7. M. VanMiddlesworth, M. Kaess, F. S. Hover, and J. Leonard, "Mapping 3D Underwater Environments with Smoothed Submaps," International Conference on Field and Service Robotics (FSR), 2013.
  8. F. Hover, R. Eustice, A. Kim, B. Englot, H. Johannsson, M. Kaess, and J. Leonard, "Advanced perception, navigation and planning for autonomous in-water ship hull inspection," Journal of Robotics Research, vol. 31, no. 12, pp. 1445-1464, 2012. https://doi.org/10.1177/0278364912461059
  9. S. C. Yu, "Development of real-time acoustic image recognition system using by autonomous marine vehicle," Journal of Ocean Engineering, vol. 25, no. 1, pp. 90-105, 2008.
  10. S. Karabchevsky, B. Braginsky, and H. Guterman, "AUV real-time acoustic vertical plane obstacle detection and avoidance," International Conference on Autonomous Underwater Vehicles (AUV), 2012.
  11. M. Fallon, J. Folkesson, H. McClelland, and J. Leonard, "Relocating underwater features autonomously using sonar-based SLAM," Journal of Ocean Engineering, vol.38, no.38, pp. 500-513, 2013. https://doi.org/10.1109/JOE.2012.2235664
  12. E. O. Belcher, W. H. Hanot and J. Burch, "Dual-Frequency identification Sonar(DIDSON)," Symposium on Underwater Technology, pp. 187-192, 2002.
  13. M. V. Sarode and P. R. Deshmukh, "Reduction of speckle noise and image enhancement of image using filtering techniques," Journal of Advancements in Technology, vol.2, no.1, pp. 30-38, 2011.
  14. D. Kuan, A. Saqchuk, T. Strand, and P. Chavel, "Adaptive restoration of images with speckle," IEEE Transactions on Acoustics, Speech and Signal processing, vol.35, no.3, pp.373-383, 1987. https://doi.org/10.1109/TASSP.1987.1165131
  15. Y. Lee, JH. Lee and HT. Choi, "A Framework of Recognition and Tracking for Underwater Objects based on Sonar Images : Part 1. Design and Recognition of Artificial Landmark considering Characteristics of Sonar Images," Journal of the Institute of Electronics and Information Engineers on System and Control, vol. 52, no. 2, pp. 422-429, 2014.
  16. Y. Lee, TG. Kim, JH. Lee and HT. Choi, "A Framework of Recognition and Tracking for Underwater Objects based on Sonar Images : Part 2. Design and Implementation of Realtime Framework using Probabilistic Candidate Selection," Journal of the Institute of Electronics and Information Engineers on System and Control, vol. 51, no. 3, pp. 630-639, 2014.
  17. Sebastian Thrun, Wolfram Burgard, and Dieter Forx, Probabilistic Robotics, The MIT Press, 2005.
  18. A. Goshtasby, "Description and Discrimination of Planar Shape Using Shape matrices," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-7, no. 6, pp. 778-743, 1985.
  19. www.youtube.com/watch?v=aYYmyf0ks7w

피인용 문헌

  1. 정밀 위치정보 데이터를 이용한 수중 하저면의 수심 정보 획득 시스템 vol.15, pp.2, 2017, https://doi.org/10.13067/jkiecs.2020.15.2.327