مکان‌یابی و ایجاد نقشه هم‌زمان در محیط گلخانه با استفاده از بینایی استریو

ماشین های کشاورزی

مقاله 3، دوره 10، شماره 2 - شماره پیاپی 20، 1399، صفحه 141-153   

نوع مقاله: مقاله پژوهشی

شناسه دیجیتال (DOI): 10.22067/jam.v10i2.78256

نویسندگان

زهرا خسروبیگی؛ شاهین رفیعی^* ؛ سید سعید محتسبی؛ امین نصیری

گروه مکانیک بیوسیستم، دانشکده مهندسی فناوری، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

مکان‌یابی و ایجاد نقشه هم‌زمان (SLAM) گامی ضروری در خودکار نمودن عملیات‌های کشاورزی و در نتیجه اعمال کشاورزی دقیق می‌باشد. در این تحقیق با استفاده از یک دوربین استریو به مکان‌یابی و ایجاد نقشه هم‌زمان در محیط گلخانه با استفاده از چارچوب راس پرداخته شد. قبل از تهیه تصاویر استریو از محیط گلخانه دوربین کالیبره شده و مسیر حرکت دوربین در گلخانه طراحی و اعمال شد. مسیر طراحی شده دارای طول 7/32 متر و تعداد تصاویر گرفته شده در این مسیر 150 جفت تصویر استریو بود. جهت ارزیابی الگوریتم طراحی شده، میزان خطای مکان‌های تخمین زده شده به‌وسیله الگوریتم با مکان‌های واقعی دوربین استریو با استفاده از فاصله اقلیدسی محاسبه شد. نتایج حاصل از این تحقیق نشان داد که الگوریتم معرفی شده دارای میانگین خطای مکان‌یابی 0679412/0 متر، انحراف معیار 0456431/0 متر و ریشه میانگین مربع خطای 0075569/0 متر برای مسافت 7/32 متری پیموده شده توسط دوربین استریو می‌باشد.

کلیدواژه‌ها

بینایی استریو؛ راس؛ گلخانه؛ مکان‌یابی و ایجاد نقشه هم‌زمان (SLAM)

مراجع

1. Audras, C., A. Comport, M. Meilland, and P. Rives. 2011. Real-time dense appearance-based slam for rgb-d sensors. In: Australasian Conf. on Robotics and Automation.

2. Auta Cheein, F. A., G. Steiner, G. P. Paina, and R. Carelli. 2011. Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection. Computers and Electronics in Agriculture 78: 195-207.

3. Barth, R., J. Hemming, and E. J. V. Henten. 2016. Design of an eye-in-hand sensing and servo control framework for harvesting robotics in dense vegetation. Biosystems Engineering 146: 71-84.

4. Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool. 2008. Speeded-up robust features (SURF). Computer Vision and Image Understanding 110: 346-359.

5. Bhatti, A. 2011. Global 3D Terrain Maps for Agricultural Applications. Pages 227-242 in Rovira-Mas F, ed. Advances in theory and applications of stereo vision. InTech. Croatia.

6. Borenstein, J., and Y. Koren. 1991. The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Transactions on Robotics and Automation 7 (3): 278-288.

7. Bradski, G., and A. Kaehler. 2008. Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc. Sebastopol, CA.

8. Craig, J. J. 2005. Introduction to robotics: mechanics and control. Pearson Prentice Hall. Upper Saddle River, New Jersey, USA.

9. Cyganek, B., and J. P. Siebert. 2009. An Introduction to 3D Computer Vision Techniques and Algorithms. John Wiley & Sons, Ltd. United Kingdom.

10. Diebel, J., K. Reutersward, S. Thrun, J. Davis, and R. Gupta. 2004. Simultaneous Localization and Mapping with Active Stereo Vision. IEEE/RSJ International Conference on Intelligent Robots and Systems pp: 3437-3443.

11. Elfes, A. 1990. Occupancy grids: Astochastic spatial representation for active robot perception. In: Proceedings of the Sixth Conference on Uncertainty in AI.

12. Eliazar, A. 2003. DP-SLAM: Fast, robust simultaneous localization and mapping without predetermined landmarks. International Joint Conference on Artificial Intelligence.

13. Eliazar, A. I., and R. Parr. 2004. DP-SLAM 2.0. Robotics and Automation. Proceedings. ICRA ’04. 2004 IEEE International Conference.

14. Estrada, C., J. Neira, and J. D. Tardos. 2005. Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments. Robotics, IEEE Transactionson 21 (4): 588-596.

15. Grisetti, G., C. Stachniss, and W. Burgard. 2005. Improving grid based SLAM with Rao blackwellized particle filters by adaptive proposals and selective resampling. In Proceedings of the IEEE international conference on robotics and automation pp: 2432-2437.

16. Grisetti, G., C. Stachniss, and W. Burgard. 2007. Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Transactions in Robotics 23: 34-46.

17. Grisetti, G., R. Kuemerle, C. Stachniss, and W. Burgard. 2010. A tutorial on graph-based SLAM. IEEE Transactions on Intelligent Transportation Systems 2: 31-43.

18. Kim, G. H., J. S. Kim, and K. S. Hong. 2005. Vision-based Simultaneous Localization and Mapping with Two Cameras. IEEE/RSJ international Conference on Intelligent Robots and Systems.

19. Kitt, B., A. Geiger, and H. Lategahn. 2010. Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme. In Intelligent Vehicles Symposium. University of California, San Diego, CA, USA.

20. Kohlbrecher, S., J. Meyer, O. von Stryk, and U. Klingauf. 2011. A flexible and scalable SLAM system with full 3D motion estimation. In Proceedings of the 2011 IEEE international symposium on safety, security and rescue robotics. Japan, pp: 155-160.

21. Kohlbrecher, S., J. Meyer, T. Graber, K. Petersen, U. Klingauf, and O. Stryk. 2013. Hector open source modules for autonomous mapping and navigation with rescue robots. TU Darmstadt, Germany, Department of Computer Science.

22. Labbe, M., and F. Michau. 2014. Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM. in IEEE/RSJ International Conference on Intelligent Robots and Systems.

23. Labbe, M., and F. Michaud. 2011. Memory management for real-time appearance-based loop closure detection. in IEEE/RSJ International Conference on Intelligent Robots and Systems.

24. Labbe, M., and F. Michaud. 2013. Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation. IEEE Transactions on Robotics 29: 734-745.

25. Langaniere, R. 2011. OpenCV 2 Computer Vision Application Programming Cookbook.

26. Leonard, J., and H. Durrant-Whyte. 1991. Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation 7: 376-382.

27. Lepej, P., and J. Rakun. 2016. Localization and mapping in a complex field environment. Biosystems engineering 150: 160-169.

28. Li, M. H., B. R. Hong, Z. S. Cai, S. H. Piao, and Q. C. Huang. 2008. Novel indoor mobile robot navigation using monocular vision. Engineering Applications of Artificial Intelligence 21: 485-497.

29. Longuet-Higgins, H. 1987. A computer algorithm for reconstructing a scene from two projections. Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, MA Fischler and O. Firschein, eds pp: 61-62.

30. Milella, A., B. Nardelli, D. Di Paola, and G. Cicirelli. 2009. Robust Feature Detection and Matching for Vehicle Localization in Uncharted Environments. In Proceedings of the IEEE/RSJ IROS Workshop Planning, Perception and Navigation for Intelligent Vehicles. Saint Louis, USA.

31. Montemerlo, M., S. Thrun, D. Koller, and B. Wegbreit. 2002. FastSLAM: a factored solution to the simultaneous localization and mapping problem. In AAAI National Conference on Artificial Intelligence

32. Mousazadeh, H. and S. Javan bakht. 2015. Mechatronics and Intelligent Systems for Off-road Vehicles. University of Tehran (1th ed.). (In Farsi).

33. Nasiri, A. 2017. Creation of pathway map in a greenhouse environment using localization of cultivation platform based on stereo machine vision. Ph. D. dissertation. University of Tehran. (In Farsi).

34. Nasiri, A., H. Mobli, S. Hosseinpour, and Sh. Rafiee. 2016. Creation greenhouse environment map using localization of edge of cultivation platforms based on stereo vision. Journal of Agricultural Machinery 7 (2): 336-349. (In Farsi).

35. Nister, D. 2003. An efficient solution to the five-point relative pose problem. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2: II-195.

36. Nister, D., O. Naroditsky, and J. Bergen. 2004. Visual odometry. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1: I-652.

37. Pierzchała, M., P. Giguère, and R. Astrupa. 2018. Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM. Computers and Electronics in Agriculture 145: 217-225.

38. ROS camera_calibration_ros software stack. Retrieved from: http://wiki.ros.org/ camera_calibration_ros.

39. Rovira-Mas, F., Q. Zhang, and J. F. Reid. 2008. Stereo vision three-dimensional terrain maps for precision agriculture. Computers and Electronics in Agriculture 60: 133-143.

40. Shalal, N., T. Low, Ch. McCarthy, and N. Hancock. 2015. Orchard mapping and mobile robot localization using on-board camera and laser scanner data fusion – Part B: Mapping and localization. Computers and Electronics in Agriculture xxx: xxx–xxx.

41. Thrun, S., W. Burgard, and D. Fox. 1998. A probabilistic approach to concurrent mapping and localization for mobile robots. Autonomous Robots 3: 18.

42. Thrun, S., W. Burgard, and D. Fox. 2005. Probabilistic robotics. Cambridge, USA: The MIT Press.

43. Vazquez-Arellano, M., D. Reiser, D. S. Paraforos, M. Garrido-Izard, Me. C. Burce, and H. W. Griepentrog. 2018. 3-D reconstruction of maize plants using a time-of-flight camera. Computers and Electronics in Agriculture 145: 235-247.

44. Zhang, Z. 1998.A flexible new technique for camera calibration. Available at: http://citeseer. IST. Psu. Edu/316762. html.

45. Zhang, Z. 1999. Flexible camera calibration by viewing a plane from unknown orientations. In Computer Vision. The Proceedings of the Seventh IEEE International Conference on. Kerkyra, Greece.