In view of the position deviation of vehicle laser scanning to obtain urban scenes in different periods， the Traditional point cloud registration methods still have the limitations of low efficiency and low robustness， and an improved point cloud registration method using rods and lane lines was proposed in this paper. Firstly， the filtered point cloud was voxel grid down-sampled， and then the cloth model was used to filter the ground points， and then the K-means unsupervised classification of non-ground point clouds was used， and then the rods were extracted as the target features， and the point cloud grayscale map and spatial density segmentation method were proposed according to the reflection intensity of the point cloud. Then， the improved iterative closest point （ICP） algorithm and normal constraint were used to use rods and lane lines as registration primitives， geometric consistency algorithms were used to eliminate wrong point pairs， and bidirectional KD-trees were used to quickly correspond to the relationship of feature points， so as to accelerate the registration speed and improve accuracy. Experiments show that it takes less than 20 s in urban point cloud scenarios with low overlap， and only 20 iterations， and the accuracy can reach 1.987 7×10^-5 meters， which can realize the efficient and accurate registration of laser point clouds in urban road scenes.