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Roll and Pitch Estimation Using Visual Horizon Recognition

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Abstract

In guidance and automated control system, especially for unmanned vehicle, attitude determination is an important element. Generally this parameter is provided by sensors like INS (Inertial Navigation Systems), but it can be also estimated with a single camera that “looks” the horizon. This work presents the project of an embedded solution that uses visual information, captured by a consumer camera, to estimate the vehicle attitude. The system is designed to be mounted on board of a ship or a sail boat, in order to record the roll and pitch angles for safety purpose or to be used for real time application (e.g. during a regatta to overlap the values of the boat attitude with video output coming from a camera mounted on the masthead framing the race field).

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References

  1. Dusha, D., Mejias, L.: Attitude observability of a loosely-coupled GPS/Visual Odometry Integrated Navigation Filter. In: Australasian Conference on Robotics and Automation (ACRA 2010) (2010)

    Google Scholar 

  2. Jones, E.S., Soatto, S.: Visual-inertial Navigation, Mapping and Localization: A Scalable Real-time Causal Approach. Int. J. Rob. Res. 30, 407–430 (2011)

    Google Scholar 

  3. Dusha, D., Boles, W., Walker, R.: Attitude Estimation for a Fixed-Wing Aircraft Using Horizon Detection and Optical Flow. In: 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp. 485-492 (2007)

    Google Scholar 

  4. Todorovic, S., Nechyba, M.C.: Sky/ground modeling for autonomous mav flight. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1422–1427 (2003)

    Google Scholar 

  5. Neto, A.M., Victorino, A.C., Fantoni, I., Zampieri, D.E.: Robust horizon finding algorithm for real-time autonomous navigation based on monocular vision. In: 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 532–537 (2011)

    Google Scholar 

  6. Ettinger, S.M., Nechyba, M.C., Ifju, P.G., Waszak, M.: Vision-guided flight stability and control for micro air vehicles. In: IEEE/RSJ International Conference on Intelligent Robots and Systems 2133, pp. 2134–2140 (2002)

    Google Scholar 

  7. Boroujeni, N.S., Etemad, S.A., Whitehead, A.: Robust Horizon Detection Using Segmentation for UAV Applications. In: Ninth Conference on Computer and Robot Vision (CRV), pp. 346–352 (2012)

    Google Scholar 

  8. Oreifej, O., Lobo, N., Shah, M.: Horizon constraint for unambiguous UAV navigation in planar scenes. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1159–1165 (2011)

    Google Scholar 

  9. Cornall, T., Egan, G., Cornall, T.D., Egan, G.K.: Measuring Horizon Angle from Video on a Small Unmanned Air Vehicle. In: 2nd International Conference on Autonomous Robots and Agents (2004)

    Google Scholar 

  10. Walia, R., Jarvis, R.A.: Horizon detection from pseudo spectra images of water scenes. In: IEEE Conference on Cybernetics and Intelligent Systems (CIS), pp. 138–144 (2010)

    Google Scholar 

  11. Zafarifar, B., Weda, H., et al.: Horizon detection based on sky-color and edge features. In: Electronic Imaging 2008, pp. 680–692 (2008)

    Google Scholar 

  12. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518 (2004)

    Google Scholar 

  13. Pollefeys, M., Verbiest, F., Gool, L.J.V.: Surviving Dominant Planes in Uncalibrated Structure and Motion Recovery. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 837–851. Springer, Heidelberg (2002)

    Google Scholar 

  14. Szeliski, R., Torr, P.H.S.: Geometrically Constrained Structure from Motion: Points on Planes. In: European Workshop On 3d Structure From Multiple Images Of Large-Scale Environments, pp. 171–186 (1998)

    Google Scholar 

  15. Kummerle, R., Steder, B., Dornhege, C., Kleiner, A., Grisetti, G., Burgard, W.: Large scale graph-based SLAM using aerial images as prior information. In: Proceedings of Robotics: Science and Systems (2009)

    Google Scholar 

  16. Steder, B., Grisetti, G., Stachniss, C., Burgard, W.: Visual SLAM for Flying Vehicles. Trans. Rob. 24, 1088–1093 (2008)

    Article  Google Scholar 

  17. Angrisano, A., Gaglione, S., Gioia, C.: Performance assessment of GPS/GLONASS single point positioning in an urban environment. Acta Geodaetica et Geophysica 48, 149–161 (2013)

    Article  Google Scholar 

  18. Angrisano, A., Petovello, M., Pugliano, G.: Benefits of combined GPS/GLONASS with low-cost MEMS IMUs for vehicular urban navigation. Sensors 12, 5134–5158 (2012)

    Article  Google Scholar 

  19. Libe, T., Gershikov, E., Kosolapov, S.: Comparison of Methods for Horizon Line Detection in Sea Images. In: The Fourth International Conference on Creative Content Technologies, pp. 75–85 (2012)

    Google Scholar 

  20. Lu, J.-W., Dong, Y.-Z., Yuan, X.-H., Lu, F.-L.: An Algorithm for Locating Sky-Sea Line. In: IEEE International Conference on Automation Science and Engineering, pp. 615–619 (2006)

    Google Scholar 

  21. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)

    Article  Google Scholar 

  22. Bajaj, M., Lay, J.A.: Image Indexing and Retrieval in Compressed Domain Using Color Clusters. In: IEEE Symposium on Computational Intelligence in Image and Signal Processing. CIISP 2007, pp. 271–274 (2007)

    Google Scholar 

  23. Lloyd, S.: Least squares quantization in PCM. EEE Transactions on Information Theory archive 28, 129–137 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  24. Mignotte, M.: Segmentation by Fusion of Histogram-Based K -Means Clusters in Different Color Spaces. IEEE Transactions on Image Processing 17, 780–787 (2008)

    Article  MathSciNet  Google Scholar 

  25. Canny, J.: A Computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6), 679–698 (1986)

    Article  Google Scholar 

  26. Hough, P.V.C.: Method and Means for Recognizing Complex Patterns (1960)

    Google Scholar 

  27. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn., pp. 152–157. Prentice-Hall, Inc., Upper Saddle River (2006)

    Google Scholar 

  28. Duda, R.O., Hart, P.E.: Use of the Hough Transformation to Detect Lines and Curves in Pictures. Commun. ACM 15, 11–15 (1972)

    Article  MATH  Google Scholar 

  29. Mugnier, C.J., Forstner, W., Wrobel, B., Paderes, F., Munjy, R.: Manual of photogrammetry. American Society for Photogrammetry and Remote Sensing, pp. 215–223 (2004)

    Google Scholar 

  30. Brown, D.C.: Close-range camera calibration. Photogrammetric Engineering 37, 855–866 (1971)

    Google Scholar 

  31. Remondino, F., Fraser, C.: Digital camera calibration methods: considerations and comparisons. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (2006)

    Google Scholar 

  32. Snyder, J.P.: Map Projections Used by the U.S. Geological Survey. U.S. Department of the Interior, Geological Survey (1983)

    Google Scholar 

  33. Royal Navy: Admiralty Manual of Navigation. Stationery Office (1987)

    Google Scholar 

  34. Nocerino, E., Ackermann, S., Del Pizzo, S., Menna, F., Troisi, S.: Low-cost human motion capture system for postural analysis onboard ships. In: Proc. Spie, vol. 8085, pp. 800–815 (2011)

    Google Scholar 

  35. Cooper, M.A.R., Robson, S.: In: Atkinson, K.B., (ed.) Close Range Photogrammetry and Machine Vision pp. 9–25. Whittles Publishing (2001)

    Google Scholar 

  36. Bancroft, J.B.: Multiple IMU Integration for Vehicular Navigation. In: Proceedings of ION GNSS 2009, vol. 1, pp. 1–13 (2009)

    Google Scholar 

  37. Fefilatyev, S., Goldgof, D.B., Langebrake, L.: Toward detection of marine vehicles on horizon from buoy camera. In: Proc. SPIE 6736 Unmanned/Unattended Sensors and Sensor Networks, pp. 673–676 (2007)

    Google Scholar 

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Correspondence to Silvio Del Pizzo .

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Del Pizzo, S., Troisi, S., Angrisano, A., Gaglione, S. (2014). Roll and Pitch Estimation Using Visual Horizon Recognition. In: De Paolis, L., Mongelli, A. (eds) Augmented and Virtual Reality. AVR 2014. Lecture Notes in Computer Science(), vol 8853. Springer, Cham. https://doi.org/10.1007/978-3-319-13969-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-13969-2_27

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  • Online ISBN: 978-3-319-13969-2

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