Abstract
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.
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Bay, H., Ess, A., Tuytelaars, T., & Gool, L. V. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346–359.
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).
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.
Elibol, A., Gracias, N., & Garcia, R. (2012). Efficient topology estimation for large scale optical mapping (Vol. 82). Berlin: Springer.
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.
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.
Eustice, R. M., Pizarro, O., & Singh, H. (2008). Visually augmented navigation for autonomous underwater vehicles. IEEE Journal of Oceanic Engineering, 33(2), 103–122.
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.
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.
Förstner, W., & Wrobel, B. P. (2016). Photogrammetric Computer Vision. Berlin: Springer.
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).
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).
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.
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.
Haralick, R. M. (1996). Propagating covariance in computer vision. International Journal of Pattern Recognition and Artificial Intelligence, 10(5), 561–572.
Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge: Cambridge University Press.
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).
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.
Ila, V., Porta, J. M., & Andrade-Cetto, J. (2010). Information-based compact pose SLAM. IEEE Transactions on Robotics, 26(1), 78–93.
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).
Kaess, M., Ranganathan, A., & Dellaert, F. (2008). iSAM: Incremental smoothing and mapping. IEEE Transaction on Robotics, 24(6), 1365–1378.
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.
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.
Koning, W. D. (1984). Optimal estimation of linear discrete-time systems with stochastic parameters. Automatica, 20(1), 113–115.
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).
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.
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).
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).
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
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.
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.
Mallios, A., Ridao, P., Ribas, D., & Hernández, E. (2014). Scan matching SLAM in underwater environments. Autonomous Robots, 36(3), 181–198.
Nicosevici, T., & Garcia, R. (2012). Automatic visual bag-of-words for online robot navigation and mapping. IEEE Transactions on Robotics, 28(4), 886–898.
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.
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.
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.
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).
Särkkä, S. (2013). Bayesian filtering and smoothing. Cambridge: Cambridge University Press.
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.
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).
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.
Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. In P. Heekbert (Ed.), Graphics gems IV (pp. 474–485). Cambridge: Academic Press.
Acknowledgements
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.
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Hong, S., Kim, J. Selective image registration for efficient visual SLAM on planar surface structures in underwater environment. Auton Robot 43, 1665–1679 (2019). https://doi.org/10.1007/s10514-018-09824-1
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DOI: https://doi.org/10.1007/s10514-018-09824-1