Space positioning of mobile robot is an important link to realize unmanned driving, but using a single sensor only for localization would produce positioning error and the error keeps accumulating. In order to improve the spatial positioning accuracy of mobile robot, a spatial positioning method integrating three kinds of sensors is proposed, which uses LiDAR, inertial measurement unit (IMU) and photoelectric encoder. First, the extended Kalman filter algorithm is used to fuse the photoelectric odometer information based on distance flow algorithm, IMU yaw data information and wheel odometer information with dual photoelectric encoders; second, the differential improvement of the extended Kalman filter is used to eliminate the oscillation caused by the existence of two absolute attitude information in the fusion process; finally, the positioning accuracy after optimization is verified by experiments on a self-built driverless wheelchair mobile platform. The results of repeated experiments show that compared with the track obtained by encoder odometer, the track optimized by multi-sensor fusion significantly reduces the maximum value of positioning deviation and the mean value of absolute error, and the more complex the environment is, the better the optimization effect is, which shows that the proposed method can effectively improve the spatial positioning accuracy of driverless wheelchair.