Localization method for auxiliary transport vehicles of coal mine based on feature map

In view of the problem of vehicle autonomous real-time positioning included in the unmanned driving technology of coal mine auxiliary transport vehicles, considering the environmental characteristics of the underground roadway and the driving requirements of the vehicle, a vehicle positioning method based on feature map is proposed. The equation of motion and the measurement equation for the vehicle running in the down-hole roadway are established. The positioning algorithm is discussed using extended Kalman filtering, and the result of the vehicle location state is represented by a Gaussian distribution. The simulation results show that the mean value of the vehicle position state confidence is used as an estimate of the real state, and the uncertainty described by the root mean square value of is consistent with the statistical analysis result of the actual estimation error, and that both the quantity of features measured simultaneously by the vehicle and the value of the sampling time have effect on this uncertainty. The feature map positioning method gives the confidence representation of the real-time position of the coal mine auxiliary transport vehicle in the underground mine roadway, and obtains the real-time estimation of the vehicle position and the uncertainty of the positioning.