Descriptors in lidar closed-loop detection algorithms such as Intensity ScanContext (ISC) are easily disturbed by carrier rotation and translation variations, and their invariance is weak, which leads to poor closed-loop effect. In view of these problems, a lidar point cloud descriptor with rotation and translation invariance in an urban dynamic environment is proposed. Firstly, in terms of point cloud processing, a ground point segmentation algorithm based on angular images is used to remove ground points in point cloud data, and a dynamic target elimination algorithm based on curved voxel clustering is adopted to realize point cloud segmentation and remove dynamic targets. Secondly, Coordinate system transformation of point clouds are carried out based on quaternions collected by an inertial measurement unit, and the point clouds in all frames are unified under the same heading angle, so as to realize the rotation invariance of the descriptor. In addition, the relative position relationship between the static targets in the point clouds and the normal plane where the moving direction of the carrier is located is used to determine the rendering center of the descriptor, so as to realize the translation invariance of the descriptor. Finally, the proposed descriptor is used for closed-loop detection, and the consistency of the closed-loop detection results is verified according to the spatial structure of the point cloud data. The experimental results under the KITTI public dataset show that the proposed method can achieve closed-loop detection more quickly and accurately in an urban dynamic environment. Compared with that of the ISC algorithm, the recall rate of the proposed method is improved by 8.58 percentage points at an accuracy of 100%, and the average time consumption is reduced by 12.90%.