Navigation of autonomous unmanned underwater vehicle using stereo images with 3D modeling of environment.
https://doi.org/10.17285/0869-7035.2017.25.3.115-129
Abstract
A method of navigating an autonomous unmanned underwater vehicle, based on visual odometry is described. Modifications to the methods are proposed to enhance the accuracy of the vehicle localization and to reduce the cost of computations. This includes an algorithm with continuous tracking of image features, which increases the accuracy of vehicle local travel computation; an adaptive methodology of trajectory calculation is proposed, as well as a method of visual navigation of an underwater vehicle under conditions of local maneuvering, based on virtual coordinate referencing frame. Also, a method of 3D reconstruction of objects by images, essential during underwater inspections, is described.
About the Authors
V. A. Bobkov
Institute of Automation and Control Processes, Far-Eastern Branch of RAS, Far-Eastern State University (Vladivostok)
Russian Federation
Russian Federation
A. P. Kudryashov
Institute of Automation and Control Processes, Far-Eastern Branch of RAS, Far-Eastern State University (Vladivostok)
Russian Federation
Russian Federation
S. V. Mel’man
Institute of Automation and Control Processes, Far-Eastern Branch of RAS, Far-Eastern State University (Vladivostok)
Russian Federation
Russian Federation
A. F. Shcherbatyuk
Institute of Automation and Control Processes, Far-Eastern Branch of RAS, Far-Eastern State University (Vladivostok)
Russian Federation
Russian Federation
References
1. Aqel M., Marhaban M.H, Saripan M.I, and Ismail N.B., Review of Visual Odometry: Types, Approaches, Challenges, and Applications, SpringerPlus, 2016.
2. Bobkov V. A., Ronshin Yu. I., Kudryashov A.P., and Mashentsev V. Yu., 3D SLAM from Stereoimages. Programming and Computer Software, 2014, No. 4, pp. 159–165.
3. Bobkov, V.A., and Mashentsev, V.Yu., Navigatsiya podvodnogo robota po stereoizobrazheniyam (Underwater Robot Navigation Using Stereo Images), Mekhatronika, avtomatizatsiya, upravleniye, No. 2, vol. 17, 2016, pp. 101—109.
4. Bobkov V., MashentsevV., Tolstonogov A., and Scherbatyuk A. Adaptive Method for AUV Navigation Using Stereo Vision, Proceedings of the 26th ISOPE International Ocean and Polar Engineering Conference, Greece, 2016.
5. Goi V., Gatsenko A., Shestopalov G., Sporyshev M., Tolstonogov A., and Scherbatyuk A., Stabilization of an Autonomous Underwater Vehicle Relative to the Bottom of the Sea by Means of Stereoscopic Vision, Proceedings of the OCEANS 2015 MTS/IEEE Conference, Italy, 2015.
6. Pavin A., Inzartsev A., Eliseenko G., Lebedko O., and Panin M., Reconfigurable Web-based Simulation Environment for AUV, Proceedings of the OCEANS'15 MTS/IEEE Conference & Exhibition, Washington DC, 2015.
7. Pavin A., Underwater Object Recognition in Photo Images, Proceedings of the OCEANS'15 MTS/IEEE Conference & Exhibition, USA, 2015.
8. Pavin A., Inzartsev A., and Eliseenko G. Reconfigurable Distributed Software Platform for a Group of UUVs (Yet Another Robot Platform), Proceedings of the OCEANS 2016 MTS/IEEE Conference & Exhibition, USA, 2016.
9. Inzartsev A., Pavin A., Kleschev A., Gribova V., and Eliseenko G., Application of Artificial Intelligence Techniques for Fault Diagnostics of Autonomous Underwater Vehicles, Proceedings of the OCEANS 2016 MTS/IEEE Conference &Exhibition, USA, 2016.
10. Johnson-Roberson M., Pizarro O., Williams S. B., and Mahon I., Generation and Visualization of Large-Scale Three-Dimensional Reconstructions from Underwater Robotic Surveys, Journal of Field Robotics, Special Issue: Three-Dimensional Mapping, Part 3, 2010, vol. 27, issue 1, pp. 21–51.
11. Bobkov, V.A., Kudryashov, A.P., Postroenie modeli morskogo dna voksel’nym metodom (Sea Bottom Modeling by Voxel Technique), Podvodnye issledovaniya i robototekhnika, 2016, No. 2, pp. 13-18.
12. Stoyanov T., Mojtahedzadeh R., Andreasson H., and Lilienthal A. J., Comparative Evaluation of Range Sensor Accuracy for Indoor Mobile Robotics and Automated Logistics Applications, Robotics and Autonomous Systems, vol. 61, 2013, pp. 1094-1105.
13. Badino, H., Yamamoto, A., and Kanade, T., Visual Odometry by Multi-frame Feature Integration, International Workshop on Computer Vision for Autonomous Driving @ ICCV, 2013, pp. 222-229.
14. Lucas, B.D., and Kanade, T., An Iterative Image Registration Technique with an Application to Stereo Vision, International Joint Conference on Artificial Intelligence, 1981, pp. 674-679.
15. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L., Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, vol. 110, issue 3, 2008, pp. 346-359.
16. Bobkov, V.A., Ron’shin, Yu.I., and Mashentsev, V.Yu., Postroenie kart glubin na mnozhestvennoi posledovatel’nosti vidov (Construction of Maps of Depths Based on Multiple Sequence of Views), Informatsionnye tekhnologii, 2012, No. 6, pp. 39-44.
17. Curless B., and Levoy M., A Volumetric Method for Building Complex Models from Range Images, Computer Graphics, 1996, pp. 303-312.
18. Rusinkiewicz S., Hall-Holt O., and Levoy M., Real-time 3D Model Acquisition, ACM Trans. Graph. (Proc. SIGGRAPH), 2000, pp. 438–446.
19. Zwicker M., Pfister H., Baar J. V., and Gross M., Surface Splatting, Computer Graphics (Proc. SIGGRAPH), 2000, pp. 371–378.
20. Mel’man S., Bobkov V., Inzartsev A., and Pavin A., Distributed Simulation Framework for Investigation of Autonomous Underwater Vehicles Real-Time Behavior, Proceedings of the OCEANS'15 MTS/IEEE, 2015, pp. 43-58.
21. Geiger A., Lenz Ph., and Urtasun R., Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite, Conference on Computer Vision and Pattern Recognition (CVPR), 2012, Presentation, pp. 1-8. 22. http://www.cvlibs.net/datasets/kitti/eval_odometry.php.
Review
For citations:
Bobkov V.A., Kudryashov A.P., Mel’man S.V., Shcherbatyuk A.F. Navigation of autonomous unmanned underwater vehicle using stereo images with 3D modeling of environment. Giroskopiya i Navigatsiya. 2017;25(3):115-129. (In Russ.) https://doi.org/10.17285/0869-7035.2017.25.3.115-129
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