Abstract: The optimal information fusion Kalman filter algorithm gives the linear minimum variance in the dynamic environment. This paper applies the optimal information fusion Kalman filter algorithm to the ball state estimation and prediction in robot soccer system，and presents the processing structure of information fusion and detailed realization steps of the algorithm.The experimentation result shows that this algorithm can eliminate noises contained in single robot vision sensor data and precisely estimate and predict the state of the ball with good feasibility and advantage.Furthermore，when some robot vision sensor is faulty，it also has fault tolerance and robustness properties.

Key words: optimal information fusion, Kalman filter, robot vision sensor, robot soccer system

摘要： 最优信息融合Kalman滤波算法给出了实时动态环境中线性方差最小的融合估计。采用该算法对机器人足球系统中的小球进行状态估计和预测，并给出了信息融合处理结构和该算法的具体实现步骤。实验结果表明，该算法可以克服单一视觉传感器采集的数据含有较大噪声等局限性，实现了对小球精确的状态估计和预测，具有可行性和优越性，并且在某一机器人视觉传感器出错时，系统仍具有良好的容错性和鲁棒性。

关键词: 最优信息融合, Kalman滤波, 机器人视觉传感器, 机器人足球系统