A pose recognition method for warehouse cleaning robots based on extended Kalman filtering

The lighting intensity of coal mine water storage roadways is uneven and the structured features are obvious. Traditional vision based robot pose recognition methods are not accurate. The single robot positioning techniques such as Adaptive Monte Carlo localization (AMCL) method have significant cumulative errors in the output pose information with the long-term operation of the cleaning robot. It is easy to encounter situations where the coal slurry is not cleaned thoroughly and collides with both sides of the roadway. In order to solve the above problem, a multi-sensor fusion clearance robot pose recognition method based on extended Kalman filtering is proposed. Firstly, the method builds a multi-sensor fusion algorithm framework and establishes models for odometer, inertial measurement devices, and LiDAR data acquisition. Secondly, based on the principle of extended Kalman filtering, an observation equation is established using the angle information of the inertial measurement device. Combined with the odometer pose information, the first fusion of the clearance robot pose matrix is obtained. Then, the position information of the lidar is iterated with the previous pose matrix to obtain the second fused clearance robot pose matrix. Finally, the complementary filtering algorithm is used to process the pose matrix of the clearance robot after two fusion and output the final pose matrix of the clearance robot. The experimental results show that the maximum position error in linear pose recognition is 0.04 m, and the maximum attitude angle error is 0.05 rad. The maximum position error in the simulated roadway experiment is 0.1 m, and the maximum attitude angle error is 0.085 rad. Compared with the AMCL method, the pose recognition method of the warehouse cleaning robot based on extended Kalman filtering shows significant effectiveness in reducing the cumulative error during the operation of the warehouse cleaning robot.