Browse > Article
http://dx.doi.org/10.7746/jkros.2021.16.3.238

Image Mosaicking Considering Pairwise Registrability in Structure Inspection with Underwater Robots  

Hong, Seonghun (Department of Robot Engineering, Keimyung University)
Publication Information
The Journal of Korea Robotics Society / v.16, no.3, 2021 , pp. 238-244 More about this Journal
Abstract
Image mosaicking is a common and useful technique to visualize a global map by stitching a large number of local images obtained from visual surveys in underwater environments. In particular, visual inspection of underwater structures using underwater robots can be a potential application for image mosaicking. Feature-based pairwise image registration is a commonly employed process in most image mosaicking algorithms to estimate visual odometry information between compared images. However, visual features are not always uniformly distributed on the surface of underwater structures, and thus the performance of image registration can vary significantly, which results in unnecessary computations in image matching for poor-conditioned image pairs. This study proposes a pairwise registrability measure to select informative image pairs and to improve the overall computational efficiency of underwater image mosaicking algorithms. The validity and effectiveness of the image mosaicking algorithm considering the pairwise registrability are demonstrated using an experimental dataset obtained with a full-scale ship in a real sea environment.
Keywords
Image Mosaicking; Pairwise Registrability; Underwater Robots; Structure Inspection;
Citations & Related Records
연도 인용수 순위
  • Reference
1 K. Zuiderveld, "VIII.5. - Contrast Limited Adaptive Histogram Equalization," Graphics Gems, Academic Press, 1994, pp. 474-485, DOI: 10.1016/b978-0-12-336156-1.50061-6.
2 D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, pp. 91-110, 2004, DOI: 10.1023/b:visi.0000029664.99615.94.   DOI
3 R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2003, DOI: 10.1017/CBO9780511811685.
4 P. Ozog and R. M. Eustice, "On the importance of modeling camera calibration uncertainty in visual SLAM," 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, pp. 3762-3769, 2013, DOI: 10.1109/icra.2013.6631108.   DOI
5 W. Forstner and B. P. Wrobel, Photogrammetric Computer Vision, Springer, 2016, DOI: 10.1007/978-3-319-11550-4.
6 A. Elibol, H. Shim, S. Hong, J. Kim, N. Gracias, and R. Garcia, "Online underwater optical mapping for trajectories with gaps," Intelligent Service Robotics, vol. 9, pp. 217-229, 2017, DOI: 10.1007/s11370-016-0195-4.   DOI
7 S. Hong, D. Chung, J. Kim, Y. Kim, A. Kim, and H. K. Yoon, "In-water visual ship hull inspection using a hover-capable underwater vehicle with stereo vision," Journal of Field Robotics, vol. 36, no. 3, 2019, DOI: 10.1002/rob.21841.   DOI
8 A. Elibol, N. Gracias, and R. Garcia, "Fast topology estimation for image mosaicing using adaptive information thresholding," Robotics and Autonomous Systems, vol. 61, no. 2, pp. 125-136, Feb., 2013, DOI: 10.1016/j.robot.2012.10.010.   DOI
9 A. Kim and R. M. Eustice, "Real-Time Visual SLAM for Autonomous Underwater Hull Inspection Using Visual Saliency," IEEE Transactions on Robotics, vol. 29, no. 3, pp. 719-733, Jun., 2013, DOI: 10.1109/tro.2012.2235699.   DOI
10 S. Sarkka, Bayesian Filtering and Smoothing, Cambridge University Press, 2013, DOI: 10.1017/CBO9781139344203.
11 W. L. De Koning, "Optimal estimation of linear discrete-time systems with stochastic parameters," Automatica, vol. 20, no. 1, pp. 113-115, Jan., 1984, DOI: 10.1016/0005-1098(84)90071-2.   DOI
12 Y. Luo, Y. Zhu, X. Shen, and E. Song, "Novel data association algorithm based on integrated random coefficient matrices Kalman filtering," IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 1, pp. 144-158, Jan., 2012, DOI: 10.1109/taes.2012.6129626.   DOI
13 V. Ila, J. M. Porta, and J. Andrade-Cetto, "Information-based compact pose SLAM," IEEE Transactions on Robotics, vol. 26, no. 1, pp. 78-93, Feb., 2010, DOI: 10.1109/tro.2009.2034435.   DOI
14 S. Hong and J. Kim, "Selective image registration for efficient visual SLAM on planar surface structures in underwater environment," Autonomous Robots, vol. 43, pp. 1665-1679, 2019, DOI: 10.1007/s10514-018-09824-1.   DOI