基于概率栅格地图的移动机器人可定位性估计

Probabilistic Grid Map Based Localizability Estimation for Mobile Robots

  • 摘要: 基于广泛使用的概率栅格地图,提出了一种移动机器人可定位性估计方法.通过对定位Fisher信息矩阵进行栅格离散化,提出了静态可定位性矩阵,该矩阵适用于已知地图条件下的离线估计.在此基础上,针对在线估计中环境存在的非预期动态变化问题,采用局部感知的未知障碍物影响因子来修正静态可定位性矩阵,进而得到动态可定位性矩阵,该矩阵定量描述了机器人可定位性能力及其方向性.各种典型环境下的机器人实验结果表明了所提方法的有效性.

     

    Abstract: Based on the widely used probabilistic grid map, a localizability estimation method for mobile robots is proposed. Firstly, the Fisher information matrix (FIM) of robot localization is transformed into discrete form, and a static localizability matrix suitable for off-line estimation based on the known grid map is obtained. On this basis, the impact factor of locally sensed unkown obstacles is adopted to modify the static localizability matrix, and a dynamic localizability matrix is proposed for on-line estimation to deal with unexpected dynamic changes of environments. This matrix describes both the localizability index and localizability direction of mobile robots quantitatively. The results of real robot experiments under different typical environments demonstrate the validity of the proposed method.

     

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