Street lamp extraction is an important research direction for target extraction from point clouds obtained using vehicle-borne laser scanning. However, the upper and lower parts of street lamps always produce adhesion and occlusion, making street lamp identification difficult. Considering that the adhesion and occlusion are unlikely to occur in the lamp pole’s middle, relative position relationship between lamp burner and lamp pole is examined and a hierarchical extraction approach for street lamp point cloud considering the relative distance is introduced. First, the original point cloud is divided into the lamp burner, lamp pole, and ground layers using the cloth simulation filtering (CSF) algorithm, then the connected component analysis is used to cluster the lamp burner layer and lamp pole layer point clouds. Then, the lamp pole point cloud is extracted using the diagonal’s length in each clustering rectangle of the lamp pole layer and the fitted circle’s included area. Finally, the lamp burner point cloud is estimated based on the relative distance between the lamp burner’s center and the lamp pole’s center to extract the entire street lamp. The proposed method was tested on three datasets. The proposed method’s extracted correctness, completeness, quality, and F1 value for data 1 are 100%; the correctness, completeness, and F1 value for data 2 are 87.50% while the quality is 77.78%; the completeness and quality for data 3 are 94.74%, and the correctness is 100% while the F1 value is 97.30%. The experimental findings illustrate that this method can efficiently recognize and extract street lamps.