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计算机工程 ›› 2012, Vol. 38 ›› Issue (21): 1-4. doi: 10.3969/j.issn.1000-3428.2012.21.001

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一种新的抗外部干扰EKF-SLAM算法

吕太之   

  1. (南京理工大学计算机科学与技术学院,南京 210094)
  • 收稿日期:2012-01-04 出版日期:2012-11-05 发布日期:2012-11-02
  • 作者简介:吕太之(1979-),男,高级工程师、博士研究生,主研方向:智能机器人路径规划

A Novel EKF-SLAM Algorithm Against Outlier Disturbance

LV Tai-zhi   

  1. (College of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China)
  • Received:2012-01-04 Online:2012-11-05 Published:2012-11-02

摘要:

机器人在未知环境中探索时不仅存在传感器误差,而且经常受到外部干扰的影响。传统EKF-SLAM算法没有考虑外部干扰,会导致机器人定位的失败,为此,提出一种改进的EKF-SLAM算法。采用极坐标对比前后2次观测结果来检测是否存在外部干扰。当检测到存在外部干扰时,通过膨胀系统状态的方差扩大其不确定性,使系统状态迅速收敛到真值。仿真结果表明,该算法在移动机器人SLAM的估计精度和鲁棒性两方面均优于传统的EKF-SLAM算法。

关键词: 同时定位与地图创建, 扩展卡尔曼滤波, 外部干扰, 方差膨胀, 一致性, 移动机器人

Abstract:

There is not only sensor noise, but also outlier disturbance when a robot explores in unknown environments. The traditional EKF-SLAM algorithm does not consider the impact of outlier disturbance that may lead to positioning failure. The new algorithm detects the outlier disturbance by comparing two observations result using polar coordinates. Covariance would be inflated when disturbance is detected, so that system state of uncertainty is expanded and the state quickly converges to the true value. Simulation results show that the proposed algorithm is better than EKF-SLAM both in mobile robot SLAM accuracy and robustness.

Key words: Simultaneous Localization and Mapping(SLAM), Extended Kalman Filter(EKF), outlier disturbance, covariance inflation, consistency, mobile robot

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