中国邮电高校学报(英文) ›› 2016, Vol. 23 ›› Issue (5): 47-55.doi: 10.1016/S1005-8885(16)60057-2

• Artificial Intelligence • 上一篇    下一篇

Improved Rao-Blackwellized H∞ filter based mobile robot SLAM

罗元1,苏琴2,张毅3,郑潇峰2   

  1. 1. 重庆邮电大学光电工程学院
    2. 重庆邮电大学信息无障碍工程与机器人技术研发中心
    3. 重庆邮电大学自动化学院
  • 收稿日期:2016-03-24 修回日期:2016-09-29 出版日期:2016-10-30 发布日期:2016-10-26
  • 通讯作者: 苏琴 E-mail:grace_suqin@163.com
  • 基金资助:
    重庆市科学技术委员会资助项目基金;重庆市教委科学技术研究项目基金

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).

摘要: 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.

关键词: SLAM, Rao-Blackwellized particle filter, iterated unscented H∞ filter, ROS

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

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