Multi-sensor fusion positioning of detection robot for tailings pond flood discharge tunnel

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.