Selective image registration for efficient visual SLAM on planar surface structures in underwater environment

  • Seonghun Hong
  • Jinwhan Kim


This paper presents a computationally efficient approach that can be applied to visual simultaneous localization and mapping (SLAM) for the autonomous inspection of underwater structures using monocular vision. A selective image registration scheme consisting of key-frame selection and key-pair selection is proposed to effectively use visual features that may not be evenly distributed on the surface of underwater structures. The computational cost of the visual SLAM algorithm can be substantially reduced using only potentially effective images and image pairs by applying the proposed image registration scheme. The performance of the proposed approach is demonstrated on two different experimental datasets obtained using autonomous underwater vehicles.


Visual SLAM Selective image registration Autonomous underwater vehicle 



This research was a part of the project titled ‘Development of an autonomous ship-hull inspection system’, funded by the Ministry of Oceans and Fisheries, Korea.


  1. Bay, H., Ess, A., Tuytelaars, T., & Gool, L. V. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.CrossRefGoogle Scholar
  2. Carlevaris-Bianco, N., & Eustice, R. M. (2014). Learning visual feature descriptors for dynamic lighting conditions. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 2769–2776).Google Scholar
  3. Carrasco, P. L. N., Bonin-Font, F., & Oliver-Codina, G. (2016). Global image signature for visual loop-closure detection. Autonomous Robots, 40(8), 1403–1417.CrossRefGoogle Scholar
  4. Elibol, A., Gracias, N., & Garcia, R. (2012). Efficient topology estimation for large scale optical mapping (Vol. 82). Berlin: Springer.Google Scholar
  5. Elibol, A., Gracias, N., & Garcia, R. (2013). Fast topology estimation for image mosaicing using adaptive information thresholding. Robotics and Autonomous systems, 61(2), 125–136.CrossRefGoogle Scholar
  6. Elibol, A., Shim, H., Hong, S., Kim, J., Gracias, N., & Garcia, R. (2016). Online underwater optical mapping for trajectories with gaps. Intelligent Service Robotics, 9(3), 217–229.CrossRefGoogle Scholar
  7. Eustice, R. M., Pizarro, O., & Singh, H. (2008). Visually augmented navigation for autonomous underwater vehicles. IEEE Journal of Oceanic Engineering, 33(2), 103–122.CrossRefGoogle Scholar
  8. Eustice, R. M., Singh, H., & Leonard, J. J. (2006). Exactly sparse delayed-state filters for view-based SLAM. IEEE Transactions on Robotics, 22(6), 1100–1114.CrossRefGoogle Scholar
  9. Ferreira, F., Veruggio, G., Caccia, M., & Bruzzone, G. (2012). Real-time optical SLAM-based mosaicking for unmanned underwater vehicles. Intelligent Service Robotics, 5(1), 55–71.CrossRefGoogle Scholar
  10. Förstner, W., & Wrobel, B. P. (2016). Photogrammetric Computer Vision. Berlin: Springer.CrossRefzbMATHGoogle Scholar
  11. Garcia, R., Puig, J., Ridao, P., & Cufi, X. (2002). Augmented state Kalman filtering for AUV navigation. In Proceedings of the of the IEEE international conference on robotics and automation, Washington, DC (pp. 4010–4015).Google Scholar
  12. Gong, Y. & Sbalzarini, I. F. (2013). Local weighted Gaussian curvature for image processing. In Proceedings of the international conference on image processing (pp. 534–538).Google Scholar
  13. Gracias, N., Mahoor, M., Negahdaripour, S., & Gleason, A. (2009). Fast image blending using watersheds and graph cuts. Image and Vision Computing, 27(5), 597–607.CrossRefGoogle Scholar
  14. Grisetti, G., Rizzini, D. L., Stachniss, C., Olson, E., & Burgard, W. (2008). Online constraint network optimization for efficient maximum likelihood map learning. In Proceedings of the IEEE international conference on robotics and automation.Google Scholar
  15. Haralick, R. M. (1996). Propagating covariance in computer vision. International Journal of Pattern Recognition and Artificial Intelligence, 10(5), 561–572.CrossRefGoogle Scholar
  16. Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge: Cambridge University Press.zbMATHGoogle Scholar
  17. Hong, S., & Kim, J. (2016). Efficient visual SLAM using selective image registration for autonomous inspection of underwater structures. In Proceedings of the IEEE/OES autonomous underwater vehicles (pp. 189–194).Google Scholar
  18. Hong, S., Kim, J., Pyo, J., & Yu, S.-C. (2016). A robust loop-closure method for visual SLAM in unstructured seafloor environments. Autonomous Robots, 40(6), 1095–1109.CrossRefGoogle Scholar
  19. Ila, V., Porta, J. M., & Andrade-Cetto, J. (2010). Information-based compact pose SLAM. IEEE Transactions on Robotics, 26(1), 78–93.CrossRefGoogle Scholar
  20. Johnson-Roberson, M., Pizarro, O., & Williams, S. (2010). Saliency ranking for benthic survey using underwater images. In Proceedings of the international conference on control, automation, robotics, and vision, Singapore (pp. 459–466).Google Scholar
  21. Kaess, M., Ranganathan, A., & Dellaert, F. (2008). iSAM: Incremental smoothing and mapping. IEEE Transaction on Robotics, 24(6), 1365–1378.CrossRefGoogle Scholar
  22. Kim, A., & Eustice, R. M. (2013). Real-time visual SLAM for autonomous underwater hull inspection using visual saliency. IEEE Transactions on Robotics, 29(3), 719–733.CrossRefGoogle Scholar
  23. Kim, J., Yoon, K.-J., & Kweon, I. S. (2015). Bayesian filtering for keyframe-based visual SLAM. International Journal of Robotics Research, 34(4–5), 517.CrossRefGoogle Scholar
  24. Koning, W. D. (1984). Optimal estimation of linear discrete-time systems with stochastic parameters. Automatica, 20(1), 113–115.MathSciNetCrossRefzbMATHGoogle Scholar
  25. Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., & Burgard, W. (2011). g2o: A general framework for graph optimization. In Proceedings of the IEEE international conference on robotics and automation (pp. 3607–3613).Google Scholar
  26. Lee, Y.-J., & Song, J.-B. (2010). Autonomous salient feature detection through salient cues in an HSV color space for visual indoor simultaneous localization and mapping. Advanced Robotics, 24(11), 1595–1613.CrossRefGoogle Scholar
  27. Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011). BRISK: Binary robust invariant scalable keypoints. In Proceedings of the IEEE international conference on computer vision (pp. 2548–2555).Google Scholar
  28. Li, J., Ozog, P., Abernethy, J., Eustice, R. M., & Johnson-Roberson, M. (2016). Utilizing high-dimensional features for real-time robotic applications: Reducing the curse of dimensionality for recursive bayesian estimation. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 1230–1237).Google Scholar
  29. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.MathSciNetCrossRefGoogle Scholar
  30. Luo, Y., Zhu, Y., Shen, X., & Song, E. (2012). Novel data association algorithm based on integrated random coefficient matrices Kalman filtering. IEEE Transactions on Aerospace and Electronic Systems, 48(1), 144–158.CrossRefGoogle Scholar
  31. Mahon, I., Williams, S. B., Pizarro, O., & Johnson-Roberson, M. (2008). Efficient view-based SLAM using visual loop closures. IEEE Transactions on Robotics, 24(5), 1002–1014.CrossRefGoogle Scholar
  32. Mallios, A., Ridao, P., Ribas, D., & Hernández, E. (2014). Scan matching SLAM in underwater environments. Autonomous Robots, 36(3), 181–198.CrossRefGoogle Scholar
  33. Nicosevici, T., & Garcia, R. (2012). Automatic visual bag-of-words for online robot navigation and mapping. IEEE Transactions on Robotics, 28(4), 886–898.CrossRefGoogle Scholar
  34. Ozog, P., & Eustice, R. M. (2013). On the importance of modeling camera calibration uncertainty in visual SLAM. In Proceedings of the IEEE international conference on robotics and automation (pp. 3762–3769). Karlsruhe, Germany.Google Scholar
  35. Qin, H., Li, X., Liang, J., Peng, Y., & Zhang, C. (2016). DeepFish: Accurate underwater live fish recognition with a deep architecture. Neurocomputing, 187, 49–58.CrossRefGoogle Scholar
  36. Ridao, P., Carreras, M., Ribas, D., & Garcia, R. (2010). Visual inspection of hydroelectric dams using an autonomous underwater vehicle. Journal of Field Robotics, 27(6), 759–778.CrossRefGoogle Scholar
  37. Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In Proceedings of the IEEE international conference on computer vision (pp. 2564–2571).Google Scholar
  38. Särkkä, S. (2013). Bayesian filtering and smoothing. Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  39. Sawhney, H., Hsu, S., & Kumar, R. (1998). Robust video mosaicing through topology inference and local to global alignment. In Proceedings of the European conference on computer vision (pp. 103–119). Freiburg, Germany.Google Scholar
  40. Williams, S., & Mahon, I. (2004). Simultaneous localisation and mapping on the Great Barrier Reef. In Proceedings of the IEEE international conference on robotics and automation (pp. 1771–1776).Google Scholar
  41. Zeisl, B., Georgel, P. F., Schweiger, F., Steinbach, E., & Navab, N. (2009). Estimation of location uncertainty for scale invariant features points. In Proceedings of the British machine vision conference.Google Scholar
  42. Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. In P. Heekbert (Ed.), Graphics gems IV (pp. 474–485). Cambridge: Academic Press.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Robotics ProgramKAISTDaejeonKorea
  2. 2.Department of Mechanical EngineeringKAISTDaejeonKorea

Personalised recommendations