Enhanced SLAM for a Mobile Robot using Extended Kalman Filter and Neural Networks
This paper presents a Hybrid filter based Simultaneous Localization and Mapping (SLAM) scheme for a mobile robot to compensate for the Extended Kalman Filter (EKF) based SLAM errors inherently caused by its linearization process. The proposed Hybrid filter consists of a Radial Basis Function (RBF) and EKF which is a milestone for SLAM applications. A mobile robot autonomously explores the environment by interpreting the scene, building an appropriate map, and localizing itself relative to this map. A probabilistic approach has dominated the solution to the SLAM problem, which is a fundamental requirement for mobile robot navigation. The proposed approach, based on a Hybrid filter, has some advantages in handling a robotic system with nonlinear dynamics because of the learning property of the neural networks. The simulation and experimental results show the effectiveness of the proposed algorithm comparing with an EKF based SLAM and Multi Layer Perceptron (MLP) method.
- Kim, J. M., Kim, Y. T. and Kim, S. S., "An accurate localization for mobile robot using extended Kalman filter and sensor fusion," IEEE International Joint Conference on Neural Networks, pp. 2928-2933, 2008.
- Lee, S. J., Lim, J. H. and Cho, D. W., "Effective Recognition of Environment Using Sonar Ring Data for Localization of a Mobile Robot," Proceedings of the Korean Society of Precision Engineering Spring Conference, pp. 1-2, 2008.
- Kim, G. S., "Perception of small-obstacle using ultrasonic sensors for a mobile robot," Proceedings of the Korean Society of Precision Engineering Autumn Conference, pp. 21-24, 2004.
- Panzieri, S., Pascucci, F. and Setola, R., "Multirobot localisation using interlaced extended Kalman filter," IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2816-2821, 2006.
- Caron, F., Davy, M., Duflos, E. and Vanheeghe, P., "Particle Filtering for Multisensor Data Fusion With Switching Observation Models: Application to Land Vehicle Positioning," IEEE Transactions on Signal Processing, Vol. 55, Issue 6, pp. 2703-2719, 2007.
- Lee, S. J., Lim, J. H. and Cho, D. W., "EKF Localization and mapping by using consistent sonar feature with given minimum features," SICE-ICASE International Joint Conference, pp. 2606-2611, 2006.
- Houshangi, N. and Azizi, F., "Accurate mobile robot position determination using unscented Kalman filter," 2005 Canadian Conference on Electrical and Computer Engineering, pp. 846-851, 2005.
- Zhu, J., Zheng, N., Yuan, Z., Zhang, Q. and Zhang, X., "Unscented SLAM with conditional iterations," 2009 IEEE Intelligent Vehicles Symposium, pp. 134-139, 2009.
- Harb, M., Abielmona, R., Naji, K. and Petriul, E., "Neural networks for environmental recognition and navigation of a mobile robot," IEEE International Instrumentation and Measurement Technology Conference, pp. 1123-1128, 2008.
- Zu, L., Wang, H. K. and Yue, F., "Artificial neural networks for mobile robot acquiring heading angle," Proceedings of the Third International Conference on Machine Laming and Cybemetics, pp. 26-29, 2004.
- Bugeja, M. K. and Fabri, S. G, "Multilayer Perceptron Adaptive Dynamic Control for Trajectory Tracking of Mobile Robots," IEEE Industrial Electronics Annual Conference, pp. 3798-3803, 2006.
- Vafaeesefat, A, "Optimum Creep Feed Grinding Process Conditions for Rene 80 Supper Alloy Using Neural network," Int. J. Precis. Eng. Manuf., Vol. 10, No. 3, pp. 5-11, 2009.
- Cho, S. H., "Trajectory Tracking Control of a Pneumatic X-Y Tabel using Neural Network Based PID Control," Int. J. Precis. Eng. Manuf., Vol. 10, No. 5, pp. 37-44, 2009.
- Choi, M. Y, Sakthivel, R. and Chung, W. K., "Neural network-aided extended Kalman filter for SLAM problem," IEEE International Conference on Robotics and Automation, pp. 1686-1690, 2007.
- Stubberud, S. C., Lobbia, R. N. and Owen, M., "An Adaptive Extended Kalman Filter Using Artificial Neural Networks," Proceedings of the 34th Conference on Decision &Control, pp. 1852-1856, 1995.
- Hu, Y. H. and Hwang, J. N., "Handbook of Neural Network Signal Processing," CRC Press, pp. 3.1-3.23, 2001.
- Jang, P. S., "Neural network based position tracking control of mobile robot," M.S thesis, Department of Mechatronics, Chungnam National University, pp. 13-37, 2003.
- Oh, C. M., "Control of mobile robots using RBF network," M.S thesis, Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, pp. 4-19, 2003.
- Mehra, P. and Wah, B. W., "Artificial neural networks: Concepts and theory," IEEE Computer Society Press, pp. 13-31, 1992.
- Iiguni, Y., Sakai, H. and Tokumaru, H., "A Real-Time Learning Algorithm for a Multilayered Neural Network Based on the Extended Kalman Filter," IEEE Transactions on Signal Processing, Vol. 40, No. 4, pp. 959-966, 1992.
- Thrun, S., Burgard, W. and Fox, D., "Probabilistic Robotics," The MIT Press, pp. 309-334, 2005.
- Bailey, T., Nieto, J., Guivant, J., Stevens, M. and Nebot, E., "Consistency of the EKF-SLAM Algorithm," IEEE International Conference on Intelligent Robotics and Systems, pp.3562-3568, 2006.
- Bailey, T., http://www-personal.acfr.usyd.edu.au/tbailey.
이 논문을 인용한 문헌 (5)
- 2010. "" International journal of precision engineering and manufacturing, 11(5): 705~714
- 2011. "" International journal of precision engineering and manufacturing, 12(5): 783~790
- 2012. "" International journal of precision engineering and manufacturing, 13(3): 379~386
- 2015. "" International journal of precision engineering and manufacturing, 16(10): 2073~2080
- Yan, Rui Jun ; Choi, Youn Sung ; Wu, Jing ; Han, Chang Soo 2016. "Data Association of Robot Localization and Mapping Using Partial Compatibility Test" 한국정밀공학회지 = Journal of the Korean Society of Precision Engineering, 33(2): 129~138
유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.
원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.
NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.
- 이 논문과 함께 출판된 논문 + 더보기