Aiming at the problems of single sensor data loss,low positioning accuracy and asynchronous sensor frequency of mobile robot,lidar,IMU and wheeled odometer were used to obtain positioning information,and a combined data fusion method based on extended Kalman filter and complementary fusion was proposed.The initial positioning data were preprocessed by S-G filtering algorithm,and the extended Kalman filter fusion algorithm was used to fuse the positioning data of IMU and wheel odometer sensor to obtain the fused data 1.Then,the fused data 1 and lidar data were fused by complementary fusion algorithm to obtain the fused positioning data 2.The fused data 1 was used to correct the lidar in real time,solving the displacement deviation caused by asynchronous frequency,so as to significantly improve the positioning accuracy.Finally,the Gazebo simulation platform was used to build the mobile robot model and set the basic parameters of the sensor to verify the effectiveness and stability of the algorithm.The experimental results show that the data fusion algorithm improves the positioning accuracy and stability of nonlinear sensors,and the average positioning error is within 8 cm.