Aiming at the problems of low accuracy and easy drift of the laser odometry constructed by traditional 3D point cloud registration algorithm in complex environments, this paper proposes an adaptive laser odometry for complex environments. First, the original point cloud data was collected by 3D Lidar, and after the point cloud preprocessing, the ground segmentation method was used to complete the point cloud data segmentation and obtain the road point cloud richness information; then, the NDT algorithm was used to convert the front and rear the frame point cloud data is zoomed to the maximum extent to realize the rough registration of the point cloud data; finally, under the guidance of the environmental judgment conclusion, the appropriate ICP algorithm was selected to complete the high-precision registration of the 3D point cloud and according to the output point cloud transformation relationship built the laser odometry. Through the data set and a large number of real vehicle tests in different environments, it is concluded that the average displacement error of the laser odometry in the indoor structured environment is 0.026 m, and the average displacement error in the outdoor unstructured environment is 0.1 m. The results show that the laser odometry constructed in this paper can better adapt to complex environments and obtain more accurate 3D point cloud maps and SLAM trajectories.