Eş zamanlı konum belirleme ve haritalama probleminde yeni bir durum tahmin yöntemi olarak parçacık akış filtresi

Year 2017, Volume: 32 Issue: 4, 1255 - 1270, 08.12.2017

Erol Duymaz Abdullah Ersan Oğuz Hakan Temeltaş

https://doi.org/10.17341/gazimmfd.369697

Cited By: 5

Abstract

Son çeyrek yüzyılda ortaya çıkan Eş Zamanlı
Konum Belirleme ve Haritalama (EZKH) problemi, 2000’li yıllardan başlayarak
kara, deniz, hava platformları için uyarlanmış olmakla birlikte Kalman Filtresi
tabanlı Genişletilmiş Kalman Filtresi ve Dağıtılmış Kalman Filtresi gibi
parametrik filtre yaklaşımları yanında Parçacık Filtresi gibi non-parametrik
yöntemlerden oluşan durum tahmin yöntemleri, model ya da grafik tabanlı üst
seviye kontrol amaçlayan ve özellikle de görüntü işleyen teknikler
kullanılmıştır. Platform, araç, algılayıcı tipi ve kara, deniz, hava gibi ortam
türü başlıklarında oldukça fazla farklılıklar göstermesi nedeniyle EZKH
probleminin sınıflandırma yoluyla performans analizi ihtiyacından
bahsedilebilir. İlk kez 2009 yılında ortaya konulan parçacık akış filtresi
özellikle yüksek doğruluk ve hızlı yakınsama gibi avantajları nedeniyle ilgi
görmüştür. Bu çalışmada literatürde ilk kez olarak Parçacık Akış Filtresi
tabanlı bir EZKH yapısı filtrenin matematik temelleri, filtre analizleri,
otonom bir yer aracı ve algılayıcı modelini de içerecek şekilde verilmiştir.
Belirsizlik altında tahmin araçlarının performans analizleri ile birlikte
verilen benzetim sonuçlarına göre parçacık akış filtresi tabanlı EZKH
performansı hesaplama maliyeti nedeniyle bazı gerçek zamanlı uygulamalardaki
zorluklarına rağmen literatürde daha önce yer almış diğer tahmin yöntemleriyle
karşılaştırıldığında daha başarılı sonuçlar verdiği, özellikle belirsizlikleri
daha düşük algılayıcılar kullanan ölçüm ortamlarında parçacık filtresi
yapısında ortaya çıkan dejenerasyon sorununu ortadan kaldırması nedeniyle
tercih edilebileceği görülmüştür.

Keywords

Parçacık akış filtresi, belirsizlik altında tahmin, eş zamanlı konum belirleme ve haritalama, olasılıksal sinyal işleme, seyrüsefer

References

• 1. Smith R., Cheesman P., On the representation of spatial uncertainty, Int. J. Rob. Res., 5 (4), 56–68, 1987.
• 2. Dissanayake G., Durrant-Whyte H.F., Bailey T., A computationally efficient solution to the simultaneous localisation and map building (SLAM) problem, ICRA IEEE International Conference of Robotics and Automation, San Francisco, California USA, 1009-1014, 24-28 April, 2000.
• 3. Bailey T., Durrant-Whyte H.F., Simultaneous localization and mapping (SLAM): The Essential Algorithms, IEEE Robotics and Automation Magazine, 13 (2), 99-105, 2006.
• 4. Bailey T., Durrant-Whyte H.F., Simultaneous localization and mapping (SLAM): State of the Art, IEEE Robotics and Automation Magazine, 13 (2), 105-110, 2006.
• 5. Andrade-Cetto J., Sanfeliu A., The effects of partial observability in SLAM, ICRA IEEE International Conference of Robotics and Automation, 397-402, New Orleans, Los Angeles USA, 26 April -1 May, 2004.
• 6. Vidal-Calleja T., Andrade-Cetto J., Sanfeliu A., Conditional for suboptimal filter stability in SLAM, IEEE Inernational Conference on Intelligent Robots and Systems, 27-32, Sendai, Japan, 28 Sept -2 Oct, 2004.
• 7. Bailey T., Nieto J., Guivant J., Stevens M., Nebot E., Consistency of the EKF-SLAM algorithm, IROS IEEE/RSJ International Conference on Intelligent Robots and Systems, 3562-3568, Beijing, China, 9-15 Oct, 2006.
• 8. Kim J., Sukkarieh S., Airborne Simultaneous Localisation and Map Building, ICRA IEEE International Conference of Robotics and Automation, 406-411, Taipei, Taiwan, 14-19 Sept, 2003.
• 9. Johnson E. N., Allen D.W., Kaess M., Chowdhary F.G., Autonomous flight in GPS-denied environments using monocular vision and inertial sensors, Journal of Aerospace Information Systems, 10 (4), 172-186, April 2013.
• 10. Spruyt V., Ledda A., Philips W., Sparse optical flow regularization for real-time visual tracking, ICME IEEE International Conference on Multimedia and Expo, 1-6, San Jose, California USA, 15-19 July, 2013.
• 11. Songmin J., Xiaolin Y., Xiuzhi L., Mobile robot parallel PF-SLAM based on OpenMP, ROBIO IEEE International Conference on Robotics and Biomimetics, 508-513, Guangzhou, China, 11-14 Dec, 2012.
• 12. Welle J., Schulz D., Bachran T., Cremers A.B., Optimization techniques for laser-based 3D particle filter SLAM, ICRA IEEE International Conference on Robotics and Automation, 3525-3530, Alaska USA, May, 2010.
• 13. Xiuzhi L., Songmin J., Ke W., Xiaolin Y., Distributed parallel processing of mobile robot PF-SLAM, ACAI International Conference on Automatic Control and Artificial Intelligence, 927-930, Xiamen, China, 3-5 Mar, 2012.
• 14. Martin F., Haiyang Z., CUDA accelerated robot localization and mapping, TePRA IEEE International Conference on Technologies for Practical Robot Applications, 1-6, Woburn, MA, USA, 22-23 April 2013.
• 15. Yoona S., Hyunga S., Leea M., Roha K.S., Ahna S., Geeb A., Bunnunb P., Calwayb A., Mayol-Cuevasb W.W., Real-time 3D simultaneous localization and map-building for a dynamic walking humanoid robot, Adv. Rob., 27 (10), 759-772, 2013.
• 16. Bibby C., Reid I., A Hybrid SLAM Representation for Dynamic Marine Environments, IEEE International Conference on Robotics and Automation, 257-264, Anchorage, AK, USA, 3-7 May 2010.
• 17. Aditya A., Implementation of a 4D fast SLAM including volumetric sum of the UAV, ICST International Conference on Sensing Technology, 78-84, Kolkata, India, 18-21 Dec, 2012.
• 18. Oğuz A.E., Temeltaş H., Extended kalman filter based airborne simultaneous localization and mapping, Journal of Aeronautics and Space Technologies, 6 (2), 69-74, 2013.
• 19. Vidal-Calleja T., Andrade-Cetto J., Sanfeliu A., Estimator stability analysis in SLAM, IAV2004 IFAC Symposium on Intelligent Autonomous Vehicles, 33-37, Sendai, Japan, 28 Sept-2 Oct, 2004.
• 20. Zhang L., Meng X., Chen Y., Convergence and consistency analysis for FastSLAM, IEEE Intelligent Vehicles Symposium, 447-452, Xi'an, China, 3-5 June, 2009.
• 21. Julier S.J., Uhlman J.K., Simultaneous localisation and map building using split covariance intersectio, International Conference on Intelligent Robots and Systems, 1257-1262, Maui, HI USA, 29 Oct-3 Nov, 2001.
• 22. Castellanos J.A., Neira J., Tardos J.D., Limits to the consistency of EKF-Based SLAM, IAV2004 IFAC Symp. on Intelligent Autonomous Vehicles, Sendai, Japan, 28 Sept-2 Oct, 2004.
• 23. Huang S., Disanayake G., Convergence and consistency analysis for extended kalman filter based SLAM, IEEE Trans. Rob., 23 (5), 1036-1049, 2007.
• 24. Huang G.P., Mourikis A.I., Roumeliotis S.I., Observability-based Rules for Designing Consistent EKF SLAM Estimators, Int. J. Rob. Res., 29 (5), 502-528, April 2010.
• 25. Hesch J.A., Dimitrios G.K., Bowman S.L., Roumeliotis S.I., Observability-constrained vision-aided inertial navigation, University of Minnesota MARS Lab Technical Report, Minneapolis, MN, USA, Feb 2012.
• 26. Yang P., Efficient particle filter algorithm for ultrasonic sensor-based 2D range-only simultaneous localisation and mapping application, IET Journals & Magazines, Wireless Sensor Systems, 2 (4), 394- 401, 2012.
• 27. Mohan M., Krishna M.K., Mapping large scale environments by combining particle filter and information filter, ICARCV International Conference on Control Automation Robotics & Vision, 1000-1005, Singapore, 7-10 Dec, 2010.
• 28. Thurn S., Thayer S., Whittaker W., Baker C., Burgard W., Ferguson D., Hahnel D., Montemerlo M., Morris A., Omohundro Z., Reverte C., Autonomous exploration and mapping of abandoned mines, IEEE Rob. Autom. Mag., 11 (4), 79-91, 2004.
• 29. Guivant J., Nebot E., Optimization of the simultaneous localization and map building algorithm for real time implementation, IEEE Transaction on Robotics and Automation, 17 (3), 242-257, 2013.
• 30. Shan L., Ya H.X., Xiao L.X., Ning H.Z., Simulation research on multi-robot SLAM of information filter, Advances in Mechatronics and Control Engineering Applied Mechanics and Materials, 278, 280, January 2013.
• 31. Xiaodong L., Aouf N., Experimental research on cooperative vSLAM for UAVs, CICSyN International Conference on Computational Intelligence Communication Systems and Networks, 385-390, Madrid, Spain, 5-7 June, 2013.
• 32. Liping Q., Hongjian W., An overview of Robot SLAM problem, CECNet International Conference on Consumer Electronics, Communications and Networks, 1953-1956, XianNing, China, 16-18 April, 2011.
• 33. Cadena C., Carlone L., Carrillo H., Latif Y., Scaramuzza D., Neira J., Reid I.D., Leonard J.J., Past, Present, and Future of Simultaneous Localization and Mapping: Towards the Robust-Perception Age, IEEE Trans. Rob., 32 (6), 1309-1332, 2016.
• 34. Du J., Carlone, L., Kaouk N.M., Bona B., Indri M., A comparative study on active SLAM and autonomous exploration with particle filters, AIM IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 916-923, Budapest, Hungary, 3-7 July, 2011.
• 35. Huang S., Dissanayake G., A critique of current developments in simultaneous localization and mapping, Int. J. Adv. Rob. Syst., 13 (5), 1-13, 2016.
• 36. Monjazeb A., Sasiadek, J.Z., Necsulescu D., Autonomous navigation among large number of nearby landmarks using FastSLAM and EKF-SLAM - A comparative study, MMAR International Conference on Methods and Models in Automation and Robotics, 369-374, Miedzyzdroje, Poland, 22-25 August, 2011.
• 37. Rigatos G.G., Technical analysis and implementation cost assessment of sigma-point kalman filtering and particle filtering in autonomous navigation systems, VTC IEEE Vehicular Technology Conference, 1-5, Taipei, Taiwan, 16-19 May, 2010.
• 38. Daum F., Huang J., Particle flow for nonlinear filters, Bayesian decisions and transport, FUSION International Conference on Information Fusion, 1072-1079, Istanbul, Turkey, 9-12 July, 2013.
• 39. Ding T., Coates, M.J., Implementation of the Daum-Huang exact-flow particle filter, SSP IEEE Statistical Signal Processing Workshop, 257-260, Ann Arbor, MI, USA, 5-8 August, 2012.
• 40. Jilkov V.P., Jiande W., Huimin C., Performance comparison of GPU-accelerated particle flow and particle filters, FUSION International Conference on Information Fusion, 1095-1102, Istanbul, Turkey, 9-12 July, 2013.
• 41. Charalampidis D., Jilkov V. P., Wu J., Implementation and performance of FPGA-accelerated particle flow filter, SPIE Society of Photo-Optical Instrumentation Engineers, Signal and Data Processing of Small Targets, 9596, San Diego, California, USA, 2015.
• 42. Avcı K., Kaiser-Hamming window and its performance analysis for nonrecursive digital filter design, Journal of the Faculty of Engineering and Architecture Gazi University, 29 (4), 823-833, 2014.
• 43. Karaboğa N., Kamışlıoğlu B., A new method for quarter mirror filter bank design, Journal of the Faculty of Engineering and Architecture Gazi University, 30 (2), 297-307, 2015.
• 44. Hanafi D., Abueejela Y. M., Zakaria M.F., Wall follower autonomous robot development applying fuzzy incremental controller, Intelligent Control and Automation, 4 (1), 18-25, 2013.