The traditional angle constraint algorithm to detect lidar disorders can cause excessive cutting when facing the point cloud with a special angle or lack of point clouds. Therefore, an improved angle constraint three-dimensional lidar obstacle detection method is proposed. In this study, the point cloud is converted to a deep map, a new breakpoint detector is used to complete the initial segmentation and construct the chart structure, a point cloud collection is described, and the point cloud set that meets the cluster distance is combined by searching the graph node. Compared with traditional methods, the breakpoint detector enhances segmentation robustness. Also, the graph structure search solves overcutting caused by the lack of point clouds and accelerates clustering speed. Moreover, compared with traditional methods, the average time consumption of the proposed method is reduced by 51.4% while the average positive detection rate is increased by 11.5 percentage points.