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Appearance-based odometry and mapping with feature descriptors for underwater robots


The use of Autonomous Underwater Vehicles (AUVs) for underwater tasks is a promising robotic field. These robots can carry visual inspection cameras. Besides serving the activities of inspection and mapping, the captured images can also be used to aid navigation and localization of the robots. Visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images. It has been used in a wide variety of non-standard locomotion robotic methods. In this context, this paper proposes an approach to visual odometry and mapping of underwater vehicles. Supposing the use of inspection cameras, this proposal is composed of two stages: i) the use of computer vision for visual odometry, extracting landmarks in underwater image sequences and ii) the development of topological maps for localization and navigation. The integration of such systems will allow visual odometry, localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several underwater conditions, as illumination and noise, leading to a promissory and original visual odometry and mapping technique.


  1. 1.

    Arredondo M and Lebart K. A methodology for the systematic assessment of underwater video processing algorithms.Oceans 2005; 1:362–367.

    Google Scholar 

  2. 2.

    Bay H, Tuytelaars T, Booktitle L and Gool L Van. Surf: speeded up robust features. In:Proceedings of 9 European Conference on Computer Vision; 2006; Graz, Austria. Springer: Lecture Notes in Computer Science; 2006. P. 404–417.

    Google Scholar 

  3. 3.

    Booij O, Terwijn B, Zivkovic Z and Krose B. Navigation using an appearance based topological map. In:Proceedings of IEEE International Conference on Robotics and Automation; 2007; Roma, Italy. Amsterdam: Publications of the Universiteit van Amsterdam; 2007. p. 3927–3932.

    Google Scholar 

  4. 4.

    Centeno M.Rovfurg-II: projeto e construção de um veículo subaquático não tripulado de baixo custo. [Master thesis]. Rio Grande: Universidade Federal do Rio Grande; 2007.

    Google Scholar 

  5. 5.

    Dechter R and Pearl J. Generalized best-first search strategies and the optimality af a*.Journal of the Association for Computing Machinery 1985; 32(3):505–536.

    MATH  MathSciNet  Google Scholar 

  6. 6.

    Dijkstra EW. A note on two problems in connexion with graphs.Numerische Mathematik 1959; 1:269–271.

    MATH  Article  MathSciNet  Google Scholar 

  7. 7.

    Fischler M and Bolles R. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.Communications of the ACM 1981; 24(6):381–395.

    Article  MathSciNet  Google Scholar 

  8. 8.

    Fleischer SD.Bounded-error vision-based navigation of autonomous underwater vehicles. [PhD thesis]. Stanford: Stanford University; 2000.

    Google Scholar 

  9. 9.

    Fritzke B.Growing cell structures: a self organizing network for unsupervised and supervised learning. Berkeley: University of California; 1993. (Technical report).

    Google Scholar 

  10. 10.

    Garcia R, Cufi and Carreras M. Estimating the motion of an underwater robot from a monocular image sequence. In:Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems; 2001; Maui, Hawaii. Girona, Spain: Institute of Informatics and Applications, University of Girona; 2001. p. 1682–1687. (v. 3).

    Google Scholar 

  11. 11.

    Garcia R, Lla V and Charot F. VLSI architecture for an underwater robot vision system. In:Proceedings of IEEE Oceans Conference; 2005; Brest, France. Girona, Spain: Institute of Informatics and Applications,University of Girona; 2005. p. 674–679. (v. 1)

    Google Scholar 

  12. 12.

    Gracias N, van der Zwaan S, Bernardino A and Santos-Vitor J. Results on underwater mosaic-based navigation. In:Proceedings of IEEE Oceans Conference; 2002. Biloxi, Mississippi. Lisboa, Portugal: Instituto Superior Técnico & Instituto de Sistemas e Robótica; 2002. p. 1588–1594. (v. 3).

    Google Scholar 

  13. 13.

    Gracias N and Santos-Victor J. Underwater video mosaics as visual navigation maps.Computer Vision and Image Understanding. 2000; 79(1):66–91.

    Article  Google Scholar 

  14. 14.

    Hartley R and Zisserman A.Multiple View Geometry in Computer Vision. Cambridge: Cambridge University Press; 2004.

    MATH  Google Scholar 

  15. 15.

    Kohonen T.Self-organizing maps. Secaucus: Springer-Verlag; 2001.

    MATH  Google Scholar 

  16. 16.

    Lowe D. Distinctive image features from scale-invariant keypoints.International Journal of Computer Vision. 2004; 60(2):91–110.

    Article  Google Scholar 

  17. 17.

    Mahon I and Williams S. Slam using natural features in an underwater environment. In:Proceedings of International Conference on Control, Automation, Robotics and Vision; 2004, Kunming, China. NSW Austrália: University of Sydney; p. 2076–2081. (v. 3).

    Google Scholar 

  18. 18.

    Nicosevici T, García R, Negahdaripour S, Kudzinava M and Ferrer J. Identification of suitable interest points using geometric and photometric cues in motion video for efficient 3-d environmental modeling. In:Proceedings of International Conference in Robotic and Automation; 2007; Roma, Italy. p. 4969–4974.

  19. 19.

    Plakas K and Trucco E. Developing a real-time, robust, video tracker. In:Proceedings of MTS/IEEE Oceans Conference and Exhibition; 2000; Providence, RI, USA. Edinburgh, UK: Heriot-Watt University; 2000. p. 1345–1352. (v. 2).

    Google Scholar 

  20. 20.

    Rousseeuw P. Least median of squares regression.Journal of the American Statistics Association. 1984; 79(388):871–880.

    MATH  Article  MathSciNet  Google Scholar 

  21. 21.

    Se S, Lowe D and Little J. Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks.The International Journal of Robotics Research. 2002; 21(8):735–758.

    Article  Google Scholar 

  22. 22.

    Se S, Lowe D and Little J. Vision-based global localization and mapping for mobile robots.IEEE Transactions on Robotics. 2005; 21(3):364–375.

    Article  Google Scholar 

  23. 23.

    Shi J and Tomasi C. Good features to track. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; 1994; Seattle, WA, USA. NY, USA: Cornell University Ithaca; 1994. p. 593–600.

    Google Scholar 

  24. 24.

    Tomasi C. and Kanade T.Detection and tracking of point features. Pittsburgh: Carnegie Mellon University; 1991. (Technical report).

    Google Scholar 

  25. 25.

    Tommasini T, Fusiello A, Roberto V and Trucco E. Robust feature tracking in underwater video sequences. In:Proceedings of MTS/IEEE Oceans Conference and Exhibition; 1998; Nice, France. IT: Università di Udine; 1998. p. 46–50. (v. 1).

    Google Scholar 

  26. 26.

    Torr PHS and Murray DW. The development and comparison of robust methodsfor estimating the fundamental matrix.International Journal of Computer Vision. 1997; 24(3):271–300.

    Article  Google Scholar 

  27. 27.

    Xu X and Negahdaripour S. Vision-based motion sensing for underwater navigation and mosaicing of ocean floor images. In:Proceedings of MTS/IEEE Oceans Conference and Exhibition; 1997; Halifax, NS, Canada. Coral Gables, FL: University of Miami; 1997. p. 1412–1417. (v. 2).

    Google Scholar 

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Botelho, S.S.d.C., Drews Junior, P.L.J., Figueiredo, M.d.S. et al. Appearance-based odometry and mapping with feature descriptors for underwater robots. J Braz Comp Soc 15, 47–54 (2009).

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  • Robotics
  • Computer Vision
  • Underwater Vehicles
  • Topological Maps
  • Self-localization and mapping