Vision Based Navigation for Satellite Docking

Abstract

This article deals with the situation of a space debris or not working satellite in an unidentified pose with respect to the master satellite. Feature based monocular pose estimation vision system was presented. The results of numerical simulation were described. The results of implementation and testing of simulation intended for vision­based navigation applications such as rendezvous of satellites and formation flying is shown. In this document markerless local features based navigation system has been studied. The proposed vision navigation system satellites are able to determine the position and orientation of a target in relation to the coordinate system of the camera. It relates from the time when the satellite is visible as a small object until docking with the chaser. A modified algorithm soft Position Iterations was used to estimate the pose of the target. Visual navigation system uses a single camera. The impact of changes in illumination of the object was analysed. In order to reproduce the space conditions the laboratory stand was built. The developed method was tested experimentally for different scenarios approach satellites to each other. Comparing the ground truth position and orientation and the results obtained with the aim of vision navigation system it is worth nothing to observe accuracy of the developed method. Achieved satisfactory performance of the algorithm.

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  • [1] Jacewicz, M., Głębocki, R., Navigation for satellites, Warsaw 2016.

  • [2] Amzajerdian, F., Roback, V., Bulyshev, A., Brewster, P., Imaging flash LIDAR for safe landing on solar system bodies and spacecraft rendezvous and docking, Proceedings of the SPIE Laser Radar Technology and Applications XX and Atmospheric Propagation XII, Baltimore, MD, USA 2015.

  • [3] Woods, J., Christian, J., LIDAR – based relative navigation with respect to non­cooperative objects, Acta Astronaut, 2016.

  • [4] Chien, C., Baker, K., Pose estimation for servicing of orbital replacement units in a cluttered environment, Proceedings of the IEEE International Conference on Robotics and Automation, New Orleans, LA, USA 2004.

  • [5] Sharma, S., D’Amico, S., Comparative assessment of techniques for initial pose estimation using monocular, Acta, Astronaut, 2016.

  • [6] Mikolajczyk, K., Scale & Affine Invariant Interest Point Detectors, International Journal of Computer Vision 60(1), pp. 63-86, 2004.

  • [7] English, C., Okouneva, G., Saint-Cyr, P., Choudhuri, A., Luu, T., Real­time dynamic pose estimation systems in space lessons learned for system design and performance evaluation, Int. J. Intell. Control. Syst., 2011.

  • [8] Głębocki, R., Kicman, P., Kopyt, A., Navigation module for mobile robot.

  • [9] Opromolla, R., Fasano, G., Rufino, G:, Grassi, M., Uncooperative pose estimation with a LIDAR­based system, Acta Astronaut, 2015.

  • [10] Cropp, A., Estimating pose of known target satellite, Electronics Letters, Vol. 36, No. 15, pp. 1331-1332, 2000.

  • [11] Ho, C. A. M., Autonomous Spacecraft Docking using a Computer Vision System, Proc. 31st Conf. on Decision and Control, Tucson Arizona 1992.

  • [12] Vincent, P., 3D Model­Based Tracking For Space Autonomous Randezvouz.

  • [13] Palmerini, G., Sabatini, M., Gasbarri, P., Analysis and tests of visual based techniques for orbital rendezvous operations, In Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA 2013.

  • [14] Simulation of the Docking Phase for the SMART­OLEV Satellite Servicing Mission, 9th International Symposium on Artificial Intelligence, Robotics and Automation in Space (iSAIRAS).

  • [15] Gasbarri, P., Sabatini, M., Palmerini, G., Ground tests for vision based determination and control of, Acta Astronaut, 2014.

  • [16] Ruel, S., Luu, T., Anctil, M., Gagnon, S., Target localization from 3D data for on­orbit autonomous rendezvous and docking, Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA 2008.

  • [17] Fischler, M., Random Sample Consensus: A Paradigm for Model Fitting with Applications toImage Analysis and Automated Cartography, Comm. of ACM, Vol. 24, No. 6, pp. 381-395, 1988.

  • [18] Quan, L., Lan, Z-D., Linear N4­point pose determination, 6th International Conference on Computer Vision, Bombay 1998.

  • [19] Rosenhahn, B., Foundations About 2D­3D Pose Estimation Problem.

  • [20] Petersen, T., A Comparison of 2D­3D Pose Estimation Methods, Aalborg University, Ballerup 2008.

  • [21] Philip, N., Kumar, E., Ananthasayanam, M., Sliding Observer for a Robust Relative Position and Attitude Estimation During the Final Phase of an Autonomous DockingMission, Proc. 49th Int’l Astronautical Congress, Melbourne, Australia 1998.

  • [22] Grunert, J., Das pothenotische Problem in erweiterter Gestalt nebst über seine Anwendungen in Geodäsie, Grunerts Archiv für Mathematik und Physik, 1841.

  • [23] Fiscshler, M., Random Sample Consensus: A Paradigm for Model Fitting with Applications toImage Analysis and Automated Cartography, Comm. of ACM, Vol. 24, No. 6, pp. 381-395, 1988.

  • [24] Jasiobedzki, P., Pose Determination and Tracking for Autonomous Satellite Capture, Proceeding of the 6th International Symposium on Artificial Intelligence and Robotics & Automation in Space, Quebec 2001.

  • [25] Jörgensen, J., Harr, J., PRISMA – An Autonomous Formation Flying Mission, ESA Small Satellite Systems and Services Symposium, Sardinia 2006.

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