Aiming at the problem that the inaccurate pose calculation of the lidar odometry when mobile robots build the map in the outdoor open environment, which will make the accuracy of the simultaneous SLAM algorithm drops, an optimized SLAM algorithm based on multi-sensor fusion is designed. In terms of algorithm, the reliability of the SLAM algorithm is improved by optimizing the front-end odometry, the data of the lidar odometry is integrated with the data of several sensors which are suitable for outdoor use, such as GNSS, we achieve the lightweight of the extended Kalman filter and embed it in the LOAM algorithm technically, and improve the lidar odometry without increasing computing resource as much as possible. Based on the optimization algorithm, an actual mobile robot platform is built and the algorithm has been transplanted on it, the hardware solution of multi-sensor fusion and the method of processing extended Kalman filter in practical engineering are realized. The experimental results in real scenes show that the algorithm can be stably maintained at 10 Hz outdoor mapping after increasing the odometry calculation, and it is reliable and feasible in complex open environment and lowcost conditions in real scenes.