针对移动机器人的室内精确定位问题，设计了基于可信度的自适应联邦滤波定位方法。首先，根据移动机器人和室内环境特点，设计了一个多传感器定位系统，并对传感器进行了具体配置。为处理多传感器数据，引入联邦滤波器，并对各子滤波器进行具体设计, 在此基础上对测量数据进行融合，从而获取移动机器人的位姿估计。然后，基于具体传感器特性，根据模糊推理给出了相应传感器测量数据的可信度因子, 同时，基于可信度因子，设计了联邦滤波器的自适应融合算法，并给出了室内移动机器人的定位结果。最后，通过仿真分析验证了所设计定位算法的有效性。

Focusing on the localization problem of indoor mobile robots, a credibility based adaptive federated filter localization method is designed. Firstly, considering the features of mobile robots and the indoor environment, a localization system with multiple sensors is designed, and the sensors are configured accordingly. To handle the information from multiple sensors, a federated filter is introduced, and each sub-filter is designed. On the basis of it, data fusion is made to the measured data for obtaining the pose estimation of mobile robots. Then, considering the characteristics of a particular sensor, the credibility factors of its measured data are given by using fuzzy reasoning. Meanwhile, based on the credibility factors, an adaptive fusing algorithm of the federated filter is designed, and the localization result of indoor mobile robots is given. Finally, simulation results are given to verify the effectiveness of the designed localization algorithm.