To improve the robustness and stability of the robot navigation system in an unknown and complex environment, a Lidar/ MEMS IMU/ Odometer integrated tightly navigation algorithm is proposed. Firstly, the algorithm corrects the distortion point cloud generated by the lidar movement through the pre-integration of the MEMS IMU/ Odometer to improve the feature matching efficiency between two frames of the point cloud. Secondly, the linearly interpolation of the pre-integrated robot posture is implemented according to the timestamp to obtain the rough position change between two frames of the point cloud. This rough pose changing is used as the initial value of the optimization algorithm iteration to reduce the number of iterations of the optimization algorithm. Then, the motion constraint of MEMS IMU/ Odometer is added to the back-end optimization, and the multi-sensor joint optimization is used to improve the positioning accuracy of the robot. Finally, the simulation experiment is carried out using the data set. The indoor and outdoor opening and closing loop experiments are implemented by using the four-wheeled trolley. Experiments show that the average outdoor open-loop positioning error of this algorithm is reduced by 51. 01% and 24. 75% respectively compared with the traditional algorithms ALOAM and LEGOLOAM, respectively, and it can maintain high accuracy when the movement such as cornering is intense.