In a complex and uncertain environment,the precision and reliability of robot localization using a single sensor is poor and easy to be interfered. Aiming at this problem, the observations information from laser telemeter and ultrasonic wave sensor is fused by using square-root unscented Kalman filter (SR-UKF). According to the updated values of the states and error variances, the robots Monte Carlo location ( MCL) density function of importance is constructed, and the two sensor respective advantages is combined by making full use of the redundant information collected by various sensors. The simulation experiments show that the robot Monte Carlo Location decisions based on multi-sensor fusion have large improvement in positioning precision and robustness, and the feasibility of the method is proved.%在复杂的不确定环境里,采用单一传感器对机器人进行定位时精度较低,并且易受干扰,可靠性较差.针对这一问题,先将激光测距仪和超声波传感器得到的观测信息利用平方根无迹卡尔曼滤波( SR-UKF)进行融合.根据更新的状态值和误差方差,构造出机器人蒙特-卡洛定位(MCL)的重要性密度函数,充分利用各种传感器采集的冗余信息,综合2种传感器各自的优点.仿真实验表明:基于多传感器融合的机器人蒙特-卡洛定位决策(SR-UKF-MCL)在定位精度和鲁棒性上都有较大的提高,证明了该种方法的可行性.
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