In order to solve the problems of false detection, missing detection and poor real-time performance caused by uneven density and incomplete segmentation of point cloud in lidar obstacle detection, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is proposed to improve the effect of obstacle clustering. Firstly, the k dimensional tree (kD tree) index is established with scattered point cloud data, and the RGF (Ray Ground Filter) algorithm is used to segment the ground points after the raw data is preprocessed. Then, the traditional DBSCAN algorithm is improved to change the clustering radius of obstacles adaptively with scanning distance, and the clustering effect of long-distance obstacle point clouds is improved. The experimental results show that the proposed method can achieve good clustering for obstacles with different distances, its average time consumption is reduced by 1.18 s and its positive detection rate is increased by 19.60 percentage points compared with those of the traditional method.