A system to evaluate the accuracy of a visual mosaicking methodology

When underwater vehicles navigate close to the ocean floor, computer vision techniques can be applied to obtain motion estimates. A complete system to create visual mosaics of the seabed is described in this paper. Unfortunately, the accuracy of the constructed mosaic is difficult to evaluate. The use of a laboratory setup to obtain an accurate error measurement is proposed. The system consists on a robot arm carrying a downward looking camera. A pattern formed by a white background and a matrix of black dots uniformly distributed along the surveyed scene is used to find the exact image registration parameters. When the robot executes a trajectory (simulating the motion of a submersible), an image sequence is acquired by the camera. The estimated motion computed from the encoders of the robot is refined by detecting, to subpixel accuracy, the black dots of the image sequence, and computing the 2D projective transform which relates two consecutive images. The pattern is then substituted by a poster of the sea floor and the trajectory is executed again, acquiring the image sequence used to test the accuracy of the mosaicking system.

[1]  J. G. Semple,et al.  Algebraic Projective Geometry , 1953 .

[2]  Azriel Rosenfeld,et al.  Gray-level corner detection , 1982, Pattern Recognit. Lett..

[3]  José Santos-Victor,et al.  Underwater Video Mosaics as Visual Navigation Maps , 2000, Comput. Vis. Image Underst..

[4]  J. Santos-Victor,et al.  Automatic mosaic creation of the ocean floor , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[5]  K. Laws Textured Image Segmentation , 1980 .

[6]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  E. Geyer,et al.  Characteristics and capabilities of navigation systems for unmanned untethered submersibles , 1987, Proceedings of the 1987 5th International Symposium on Unmanned Untethered Submersible Technology.

[8]  Matti Pietikäinen,et al.  Unsupervised texture segmentation using feature distributions , 1997, Pattern Recognit..

[9]  Rama Chellappa,et al.  Unsupervised Texture Segmentation Using Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Joan Batlle,et al.  Positioning an underwater vehicle through image mosaicking , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[11]  R. L. Marks,et al.  Real-time video mosaicking of the ocean floor , 1994, Proceedings of IEEE Symposium on Autonomous Underwater Vehicle Technology (AUV'94).

[12]  Shahriar Negahdaripour,et al.  Vision-based motion sensing for underwater navigation and mosaicing of ocean floor images , 1997, Oceans '97. MTS/IEEE Conference Proceedings.

[13]  Robert J. Schilling,et al.  Fundamentals of robotics - analysis and control , 1990 .

[14]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[15]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[16]  S. Negahdaripour,et al.  3-D motion and depth estimation from sea-floor images for mosaic-based station-keeping and navigation of ROVs/AUVs and high-resolution sea-floor mapping , 1998, Proceedings of the 1998 Workshop on Autonomous Underwater Vehicles (Cat. No.98CH36290).

[17]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[18]  X. Xu,et al.  Applications of direct 3D motion estimation for underwater machine vision systems , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[19]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[20]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..

[21]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.