The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지
DOI QR코드

DOI QR Code

Model-Based Pose Estimation for High-Precise Underwater Navigation Using Monocular Vision

단안 카메라를 이용한 수중 정밀 항법을 위한 모델 기반 포즈 추정

  • Park, JiSung (Department of Mechanical Engineering, KAIST) ;
  • Kim, JinWhan (Department of Mechanical Engineering, KAIST)
  • Received : 2016.05.16
  • Accepted : 2016.07.21
  • Published : 2016.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, a model-referenced underwater navigation algorithm is proposed for high-precise underwater navigation using monocular vision near underwater structures. The main idea of this navigation algorithm is that a 3D model-based pose estimation is combined with the inertial navigation using an extended Kalman filter (EKF). The spatial information obtained from the navigation algorithm is utilized for enabling the underwater robot to navigate near underwater structures whose geometric models are known a priori. For investigating the performance of the proposed approach the model-referenced navigation algorithm was applied to an underwater robot and a set of experiments was carried out in a water tank.

Keywords

References

  1. G. Marani, S. Choi, and J. Yuh, "Underwater Autonomous Manipulation for Intervention Missions AUVs," Ocean Engineering. Special Issue: AUV, vol.36, no.1, pp.15-23, 2009.
  2. P.J. Sanz, P. Ridao, G. Oliver, G. Casalino, Y. Petillot, C. Silvestre, C. Melchiorri, and A. Turetta, "TRIDENT: An european project targeted to increase the autonomy levels for underwater intervention missions," in OCEANS'13 MTS/IEEE, 2013.
  3. N. Palomeras, A. Pnalver, M. Massot-Campos, G. Vallicrosa, P.L. Negre, J.J. Fernandez, P. Ridao, P.J. Sanz, G. Oliver-Codina, and A. Palomer, "I-AUV Docking and Intervention in a Subsea Panel," in IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, USA, 2014.
  4. N. Palomeras, A. Carrera, N. Hurtós, G.C. Karras, C.P. Bechlioulis, M. Cashmore, D. Magazzeni, D. Long, M. Fox, K.J. Kyriakopoulos, P. Kormushev, J. Salvi, and M. Carreras, "Toward persistent autonomous intervention in a subsea panel," Autonomous Robots DOI 10.1007/s10514-015-9511-7.
  5. A.I. Comport, E. Marchand, M. Pressigout, and F. Chaumette, "Real-Time Markerless Tracking for Augmented Reality: The Virtual Visual Servoing Framework," IEEE Transactions on Visualization and Computer Graphic, vol.12, no.4, July/august, 2006.
  6. P. Boutthemy, "A Maximum Likelihood Framework for Determining Moving Edges," IEEE Transactions On Pattern Analysis And Machine Intelligence, vol.11. no.5, May, 1989.
  7. X. Gratal, C. Smith, M, Bjorkman, and D. Kragic, "Intergrating 3D Features and Virtual Visual Servoing for Hand-Eye and Humanoid Robot Pose Estimation," IEEE-RAS Int. conf. on Humanoid Robotics, pp.240-245, 2013.
  8. B. Espiau, F. Chaumette, and P. Rives, "A New Approach to Visual Servoing in Robotics," IEEE Transactions On Robotics And Automation, vol.8. no.3, June, 1992.
  9. E. Marchand, F. Spindler, and F. Chaumette, "ViSP for visual servoing: a generic software platform with a wide class of robot control skills," IEEE Robotics and Automation Magazine, Special issue on "Software Packages for Vision-Based Control of Motion", P. Oh, D. Burschka (Eds.), vol.12, no.4, pp.40-52, December, 2005. https://doi.org/10.1109/MRA.2005.1577023
  10. E. Rosten and T. Drummond, "Machine Learning for High Speed Corner Detection," Proc. Ninth European Conf. Computer Vision, vol.1, pp.430-443, 2006.
  11. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. "ORB: an efficient alternative to SIFT or SURF," IEEE International Conference on Computer Vision, pp.2564-2571, 2011.