Multi-sensor fusion positioning of detection robot for tailings pond flood discharge tunnel
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摘要: 尾矿库排洪隧洞内环境复杂，现有探测机器人定位算法存在弱纹理室内场景定位失效的问题，难以适用于该类环境下探测机器人的精确定位和累计误差消除。针对上述问题，提出了一种适用于尾矿库排洪隧洞环境的探测机器人多传感器融合定位算法。该算法基于测程法和图论，利用ArUco码将排洪隧洞环境的远距离定位简化为多段短距离定位。首先利用UMBmark算法进行里程计标定，有效消除探测机器人轮径和轴径两类系统误差；然后利用扩展卡尔曼滤波（EKF）算法融合里程计和惯导模块（IMU）信息，利用测程法实现运动过程中探测机器人位置和姿态信息计算；最后利用ArUco码作为路标并固定于隧洞内部，探测机器人携带标定后的相机对ArUco码信息进行识别与处理，利用各传感器量测值形成约束，结合图优化方法实现位姿优化，并根据ArUco码携带的信息消除累计误差，从而实现狭长空间弱纹理场景下探测机器人的远距离高精度定位。在尾矿库排洪隧洞实际场景中运行和关闭多传感器融合定位算法，分别进行10组行进40 m的重复定位实验，结果表明：多传感器融合定位算法具有较高的稳定性和精确性，能有效校正累计误差，实现探测机器人在排洪隧洞环境中的精确定位，行进20 m的平均定位误差为19.77 cm，40 m的平均定位误差为21.23 cm，且行进20 m校正后误差均值为4.2 mm。Abstract: The environment in the flood discharge tunnel of tailings pond is complex, and the existing positioning algorithm of detection robot has the problem of positioning failure in weak texture indoor scene, which is difficult to be applied to the precise positioning and accumulative error elimination of detection robot in this kind of environment. In order to solve the above problems, a multi-sensor fusion positioning algorithm of detection robot for tailings flood discharge tunnel is proposed. Based on the odometry method and graph theory, the algorithm simplifies the long-distance positioning in the flood discharge tunnel environment to multi-segment short-distance positioning by using the ArUco code. Firstly, the odometer is calibrated by UMBmark algorithm, which effectively eliminates two types of system errors of wheel diameter and axle diameter. Secondly, the extended Kalman filter (EKF) algorithm is used to fuse the information of the odometer and the inertial measurement unit (IMU), and the odometry method is used to realize the calculation of the position and attitude information of the detection robot during the movement process. Finally, the ArUco code is used as a road sign and fixed inside the tunnel. The detection robot carries the calibrated camera to identify and process the ArUco code information. The robot uses the measurement values of each sensor to form constraints, and combines constraints with the graph optimization method to achieve position and attitude optimization. And according to the information carried by the ArUco code, the accumulative error is eliminated so as to realize the long-distance high-precision positioning of the detection robot in the narrow and long space and weak texture scene. The multi-sensor fusion positioning algorithm is operated and closed in the actual scene of the tailings pond flood discharge tunnel, and 10 groups of repeated positioning experiments traveling 40 m are carried out respectively. The results show that the multi-sensor fusion positioning algorithm has high stability and precision, can correct the accumulative error effectively and realize the precise positioning of the detection robot in the flood discharge tunnel environment. The average positioning error of traveling 20 m is 19.77 cm, the average positioning error of traveling 40 m is 21.23 cm, and the average corrected error of traveling 20 m is 4.2 mm.
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图 1 探测机器人多传感器融合定位算法
Figure 1. Detection robot multi-sensor fusion positioning algorithm
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图 4 尾矿库重复定位实验环境和实验平台
Figure 4. Experimental environment and platform for repeated positioning of tailings pond
表 1 探测机器人定位误差
Table 1 Positioning error of detection robot
定位位置 平均定位误差/cm 关闭算法实验组 运行算法实验组 行进20 m 22.51 19.77 行进40 m 58.11 21.23 
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