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Single Camera-based SLAM and Local Path Planning for an Omni-directional Wheeled Robot
|關鍵字:||SLAM;相機;Local Path Planning;同時定位;路徑規劃||出版社:||機械工程學系所||引用:||1. H. Durrant-Whyte and T. Bailey, “Simultaneous Localization and mapping (SLAM): Part I the Essential Algorithms,” Robotics and Automation Magazine, pp. 99-110, June, 2006. 2. T. Bailey and H. Durrant-Whyte, “Simultaneous Localisation and Mapping (SLAM): Part II State of the Art,” Robotics and Automation Magazine, pp. 108-117, September, 2006. 3. T. Bailey, “Mobile Robot Localization and Mapping in Extensive Outdoor Environments,” PhD Thesis, The University of Sydney, Australian Centre for Field Robotics, 2002. 4. S. Se, D. Lowe, and J. Little, “Mobile Robot Localization and Mapping With Uncertainty Using Scale-Invariant Visual Landmarks,” Int. J. of Robotics Research, Vol. 21, No. 8, pp. 735-758, August 2002. 5. M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A Factored Solution to Simultaneous Mapping And Localization Problem,” Proc. 18th Natl. Conf. on Artificial Intelligence, pp. 593-598, 2002. 6. A. J. Davison, I. D. Reid, N. D. Molton and O. Stasse, “MonoSLAM: Real-Time Single Camera SLAM,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 29, No. 6, pp.1052-1067, June 2007. 7. J. Borenstein and Y. Koren, “Real-Time Obstacle Avoidance For Fast Mobile Robots,” IEEE Trans. on Sys., Man, and Cybernetics, Vol. 1, No. 5, pp. 1179-1187, 1989. 8. K.H. Kim and H.S. Cho, “Mobile Robot Navigation Based on Optimal Via-Point Selection Method,” Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Sys., pp. 1242-1247, 1998. 9. A. Fatmi, A. A. Yahmadi, L. Khriji, and N. Masmoudi, “A Fuzzy Logic Based Navigation of a Mobile Robot,” Proc. of World Academic of Science, Engineering and Technology, Vol. 15, October 2006. 10. K.H. Kim and H.S. Cho, “An Obstacle Avoidance Method for Mobile Robots Based On Fuzzy Decision-Making,” Robotica, Vol. 24, pp. 567-578, Cambridge University, 2006. 11. M. Xie, Fundamentals of Robotics: Linking Perception to Action, World Scientific, 2003. 12. R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision, McGraw-Hill, 1995. 13. Z. Zhang, “Flexible Camera Calibration by Viewing A Plane from Unknown Orientation,” in 7th IEEE Int. Conf. Computer Vision, pp. 666-673, 1999. 14. J. Heikkilä and O. Silvén, “A Four-step Camera Calibration Procedure with Implicit Image Correction,” In Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 1106-1112, 1997. 15. J. Sola, A. Monin, M. Devy and T. Lemaire,”Undelayed Initialization in Bearing Only SLAM,” in Proc. IEEE/RSJ Conference on Intelligent Robots and Systems, 2005. 16. J. Shi, and C. Tomasi, “Good Features to Track,” In Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, Seattle, USA, pp. 593-600, 1994. 17. A. J. Davison and D. W. Murray,“ Simultaneous Localisation and Map-Building Using Active Vision,” IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 865-880, July 2002. 18. P. M. Newman, “On the Structure and Solution of the Simultaneous Localisation and Map Building Problem,” PhD Thesis, The University of Sydney, Sydney, March1999. 19. M.I.Ribeiro, “Kalman and Extended Kalman Filters : Concept, Derivation and Properties,” Technical Report, Institute for Systems and Robotics, Portugal, 2004. 20. R. G. Brown and P. Y . C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, John Willey&Sons, 3rd Ed., 1997. 21. J. M. M Montiel, J. Civera, and A. J. Davison, “Unified Inverse Depth Parametrization for Monocular SLAM,” In Robotics Science and Systems, 2006. 22. K. S. Fu, R. C. Gonzalez, and C. S. G. Lee, Robotics, Control, Sensing, Vision, and Intelligence, McGraw-Hill, 1987. 23. H. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. E. Kavraki and S. Thrun, Principles of Robot Motion : Theory, Algorithms and Implementations, The MIT Press Cambrige, England, 2005. 24. Lowe, “Distinctive Image Features From Scale-Invariant Keypoints,” Int. Journal of Computer Vision, pp. 91-110, 2004. 25. C. Harris, and M. Stephens, “A Combined Corner and Edge Detector,” In Fourth Alvey Vision Conference, pp. 147-151, Manchester, UK, 1988. 26. P. P. Smith, Active Sensors For Local Planning in Mobile Robotics, World Scientific, 2001. 27. H. J. Zimmermann, Fuzzy Set Theory and Its Applications, Second Edition, Kluwer Academic Publishers, 1991. 28. D. Shreiner, M. Woo, J. Neider, and T. Davis, OpenGL Programing Guide, Fifth Edition, Addison-Wesley, 2006. 29. J. Smart, K. Hock and S. Csomor, Cross-Platform GUI Programming with wxWidgets, Prentice Hall, 2006.||摘要:||
本研究旨在探討移動式機器人的同時定位與建圖(SLAM)演算法，以及模糊局部路徑自動規劃相關課題。首先，根據 Davison等人所提的 MonoSLAM演算法，只使用單一攝影機來實現SLAM技術。由攝影機取得的影像和機器人速度數據，使用標準延伸式卡門濾波器(extended Kalman filter)進行機器人方位的估測與增減標的物的建圖工作。並運用模糊決策原理進行移動式機器人的自動局部路徑規劃，當實現SLAM時，局部路徑規劃的策略乃是為了讓機器人選擇一條適當的路徑來避開障礙物而設計的。為了可以頻率30HZ的取影速率即時執行 MonoSLAM演算法，本研究撰寫具圖形使用者介面(GUI)的C++程式，其中使用OpenGL演算法來畫圖、OpenCV進行影像處理，以及SceneLib的一些副程式。
In this thesis, we study about the solution for the navigation problem in mobile robotics by combining Simultaneous Localization and Mapping (SLAM) algorithm and local path planning based on fuzzy decision-making theory. First, we adopt the MonoSLAM frame work proposed by Davison et al.  to solve the SLAM problem. This algorithm uses only a single camera without odometry information for executing SLAM. The camera (robot) motion estimation and incremental map building (from new landmarks) are computed within a standard extended Kalman filter frame work. Next, we consider the local path planning based on fuzzy decision-making theory proposed in . The local path planning strategy is designed for an omni-directional robot for choosing its way to avoid obstacles when executing SLAM. For real-time implementing the MonoSLAM, we write a C++ program with graphical user interface (GUI) . In which, we use some library such as OpenGL for graphical object , OpenCV for image processing, and some subroutines from SceneLib .
Some simulations to test the MonoSLAM algorithm and the real time experiment results using USB-camera carried by an omni-directional wheeled robot are represented for illustrating the performance of the strategy. Finally, some simulations of local path planning based on SLAM's data are also conducted with different fuzzy parameters to study the navigation behavior of the robot.
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