The moving objects and structural deformation in dynamic environments bring the degradation of autonomous positioning accuracy of lidar. To address this issue, a Dynamic Lego-loam method is proposed in this article. To reduce the error caused by the mismatch of dynamic points to the lidar odometry, a point cloud coarse registration method is firstly proposed, which is based on dynamic point culling before the odometer′s precise calculation. The accuracy of laser odometry is improved. Then, to reduce the error accumulation and mapping ghosting caused by the dynamic environment, the traditional radius-based closed-loop detection method is optimized by the scene matching method. The radius-based rough search is used to quickly locate the local scene in a large range. The regional height difference descriptor is established in a small range to accurately match the most similar historical frames, which realizes an accurate closed-loop detection and improves the mapping accuracy in the dynamic environment. Compared with the Lego-loam algorithm, experimental results show that the Dynamic Lego-loam algorithm improves the autonomous positioning accuracy by 63% in a dynamic environment.