Robust localization algorithms for outdoor mobile robot
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摘要: 以Pioneer3-AT型室外轮式移动机器人为平台,采用码盘、陀螺仪、电子罗盘和GPS对机器人进行定位,设计了一种新型的联合卡尔曼滤波器,以逐级滤波的方式融合多传感器信息.首先将里程计和陀螺仪相融合,然后将融合后的定位信息与电子罗盘相融合,最后与载波相位差分GPS(RTK-GPS)相融合.该滤波器可以补偿传感器的累积误差,消除输出波动,实现机器人较高精度的定位.城市环境下RTK-GPS经常因为建筑物的遮挡而失去差分状态,定位精度不稳定,滤波器根据RTK-GPS定位精度的不同状态,选取不同的系统量测噪声协方差矩阵,使算法可以自动适应RTK-GPS传感器定位精度的变化,因此定位方法具有鲁棒性.实验结果表明,机器人可以稳定地实现0.4m的定位精度.Abstract: On the basis of Pioneer3-AT wheeled outdoor mobile robot, coder, gyro, compass and RTK-GPS were used for the robot localization. A new kind of federated Kalman filter was designed in which data from multiple sensors were fused stage by stage. Data of odometry and gyro were fused firstly. Then the fusion data were fused with data of compass. Data of RTK-GPS were fused finally. This filter could filter the fluctuation and compensate error accumulation of sensors and could achieve good precision of robot localization. In city environment, RTK-GPS would lose difference state frequently because of the shelter of buildings, so the precision of RTK-GPS was not steady. This filter could choose different measurement error covariance matrix by the precision of RTK-GPS, which made the filter adapt well to the change of RTK-GPS precision automatically, so the localization algorithm was robust. Experiment results show that robot can achieve the localization precision about 0.4 m steadily.
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Key words:
- mobile robot /
- localization /
- federated Kalman filter /
- multiple sensor fusion /
- robust /
- GPS
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