Vision-based localization and mapping system for AUV intervention

This paper presents a modular localization and mapping system for intervention autonomous underwater vehicles working in semi-structured environments with known landmarks. The system is divided in several modules to make it as generic as possible. Two visual detection algorithms can be used to compute the position of known landmarks by comparing the images taken by the vehicle against an a priori known template. Navigation data, provided by standard navigation sensors, is adapted and merged together with landmark positions by means of an extended Kalman filter. This filter is capable of estimating vehicle position and linear velocity as well as the position of detected landmarks in real-time. Experiments performed with the Girona 500 AUV in a water tank demonstrate the proposed method.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  John J. Leonard,et al.  Pure range-only sub-sea SLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[3]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[4]  Joaquim Salvi,et al.  Automatic detection of underwater chain links using a forward-looking sonar , 2013, 2013 MTS/IEEE OCEANS - Bergen.

[5]  Robert M. Haralick.  Propagating covariance in computer vision , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[6]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[7]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[8]  Marc Carreras,et al.  Girona 500 AUV: From Survey to Intervention , 2012, IEEE/ASME Transactions on Mechatronics.

[9]  William H. Press,et al.  Numerical Recipes in FORTRAN - The Art of Scientific Computing, 2nd Edition , 1987 .

[10]  Y. Petillot,et al.  Visual SLAM for underwater vehicles using video velocity log and natural landmarks , 2008, OCEANS 2008.

[11]  José Santos-Victor,et al.  Trajectory reconstruction with uncertainty estimation using mosaic registration , 2001, Robotics Auton. Syst..

[12]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[13]  Vincent Dupourqué,et al.  A robot operating system , 1984, ICRA.

[14]  Hirokazu Kato,et al.  Marker tracking and HMD calibration for a video-based augmented reality conferencing system , 1999, Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99).

[15]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[16]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[17]  David Ribas,et al.  Vision based localization system for AUV docking on subsea intervention panels , 2009, OCEANS 2009-EUROPE.

[18]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[19]  David Ribas Romagós.  Underwater slam for structured environments using an imaging sonar , 2008 .

[20]  Daniel E. Clark,et al.  Incorporating track uncertainty into the OSPA metric , 2011, 14th International Conference on Information Fusion.