Improved Rao-Blackwellized H∞ filter based mobile robot SLAM

doi:10.1016/S1005-8885(16)60057-2

JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2016, Vol. 23 ›› Issue (5): 47-55.doi: 10.1016/S1005-8885(16)60057-2

• Artificial intelligence • Previous Articles Next Articles

Improved Rao-Blackwellized H∞ filter based mobile robot SLAM

Luo Yuan, Su Qin , Zhang Yi, Zheng Xiaofeng

Received:2016-03-24 Revised:2016-09-29 Online:2016-10-30 Published:2016-10-26
Contact: Qin Su E-mail:grace_suqin@163.com
Supported by:
the Scientific and Technological Research Project Funds of Chongqing Municipal Education Commission (KJ130512), the Project Funds of Chongqing Science and Technology Commission (cstc2015jcyjB0241).

Abstract

Abstract: For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping (SLAM), a novel SLAM based on improved Rao-Blackwellized H∞ particle filter (IRBHF-SLAM) algorithm is proposed. The iterated unscented H∞ filter (IUHF) is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor’s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM (UFastSLAM) under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system (ROS), runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX (URG) laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional (2-D) grid-map with high precision in different environments.

Key words: SLAM, Rao-Blackwellized particle filter, iterated unscented H∞ filter, ROS

CLC Number:

TP242.6

Luo Yuan, Su Qin,Zhang Yi, Zheng Xiaofeng. Improved Rao-Blackwellized H∞ filter based mobile robot SLAM[J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2016, 23(5): 47-55.

References

1. Chen B F, Cai Z X, Hu D W. Approach of simultaneous localization and mapping based on local maps for robot. Journal of Central South University of Technology, 2006, 13(6):713-716

2. Chen C, Cheng Y H. Simulation research of SLAM algorithm based on iterated unscented Kalman filter. Journal of System Simulation, 2012, 24(8): 1643-1650 (in Chinese)

3. Montemerlo M, Thrun S, Koller D, et al. FastSLAM: a factored solution to the simultaneous localization and mapping problem. Proceedings of the AAAI National Conference on Artificial Intelligence (AAAI’02), Jul 28-Aut 1, 2002, Edmonton, Canada. Menlo Park ,CA, USA: American Association for Artificial Intelligence, 2003: 593-598

4. Grisetti G, Stachniss C, Burgard W. Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Transactions on Robotics, 2007, 23(1): 34-46

5. Mallios A, Ridao P, Ribas D, et al. EKF-SLAM for AUV navigation under probabilistic sonar scan-matching. Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots & Systems (IROS’10), Oct 18-22, 2010, Taipei, China. Piscataway, NJ, USA: IEEE, 2010: 4404-4411

6. Wang H J, Wang J, Liu Z Y. Fast simultaneous localization and mapping based on iterative extended Kalman filter proposal distribution and linear optimization resampling. Journal of Electronics and Information Technology, 2014, 36(2): 318-324 (in Chinese)

7. Kim C, Sakthivel R, Chung W K. Unscented FastSLAM: a robust and efficient solution to the SLAM problem. IEEE Transactions on Robotics, 2008, 24(4): 808-820

8. Zhao L J, Sun L N, Li R F, et al. On an improved SLAM algorithm in indoor environment. Robot, 2009, 31(5): 438-444 (in Chinese)

9. Hu J S, Lee M T, Yang C H. Robust adaptive beamformer for speech enhancement using the second-order extended filter. IEEE Transactions on Audio Speech and Language Processing, 2013, 21(1): 39-50

10. Okawa Y, Namerikawa T. Simultaneous localization and mapping problem via the H_∞ filter with a known landmark. Proceedings of the SICE Annual Conference (SICE’13), Sept 14-17, 2013, Nagoya, Japan. Piscataway, NJ, USA: IEEE, 2013:1939-1944

11. Ahmad H, Namerikawa T. Covariance inflation efficiency in H_∞ filter based SLAM. Proceedings of the 2011 International Conference on Electrical, Control and Computer Engineering, Jun 21-22, 2011, Pahang, Malaysia. Piscataway, NJ, USA: IEEE, 2011: 136-141

12. Pham V C, Juang J C. Robust and efficient slam via compressed H_∞ filtering. Asian Journal of Control, 2014, 16(3): 878-889

13. Song Y, Li Q L, Kang Y F. Conjugate unscented FastSLAM for autonomous mobile robots in large-scale environments. Cognitive Computation, 2014, 6(3): 496-509

14. Zhang J W, Zhang L W, Hu Y, et al. Open source robot operation system. Beijing, China: Science Press, 2012 (in Chinese)

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Improved Rao-Blackwellized H∞ filter based mobile robot SLAM

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