Aiming at the problems of imperfect point cloud feature extraction and low quality in traditional laser radar mapping algorithm, and the deviation of pose data caused by noise affecting the mapping effect, this paper proposes a laser mapping method based on improved Cartographer algorithm. Firstly, in the sensor information fusion part, the Adaptive Lossless Kalman Filter (AUKF) method was used to predict and update the sensor data, and then the noise was adaptively optimized to reduce the influence of noise on the pose data. Secondly, when processing the point cloud data collected by lidar, the effect of voxel filtering was improved, and the secondary screening of point cloud information was carried out by the method of point cloud weighting filtering to reduce the redundancy of point cloud and improve the quality of point cloud. Finally, the mapping test was carried out in the real environment, and the mapping effect of the improved algorithm and the traditional algorithm was compared. In the outdoor environment, the absolute translation error of the improved algorithm was reduced by 25.8% and the absolute rotation error was reduced by 28.9% compared with the original algorithm. It can be clearly seen that the data error of the improved algorithm is smaller and the mapping effect is more accurate.