Point cloud filtering is a necessary step in the application of airborne LiDAR point cloud post-processing. Most existing point cloud filtering methods have a better filtering effect in areas with flat terrain but a poor filtering effect in areas with high terrain fluctuation. To improve the accuracy of point cloud filtering methods and their adaptability to complex environments, this paper proposes a filtering method based on multiconstrained connected graph segmentation. In this paper, three constraint conditions, verticality, height difference, and distance, were set to construct the point cloud connectivity graph to achieve point cloud segmentation, and the ground seed point set was acquired and screened based on the ground coverage rate and the grid elevation. Finally, the ground point set optimization was realized based on the distance between the points and the adjacent ground seed point set. To test the filtering effect, 15 sets of point cloud data published on the website of the International Society of Photogrammetry and Remote Sensing (ISPRS) were used. The experiment results show that the proposed method can produce good filtering results in various terrain environments. Compared with the other four filtering methods, the proposed method has the lowest average total error (5.44% ). In addition, the average type Ⅰ error and the average type Ⅱ error of the proposed method are relatively small, indicating that the proposed method can effectively protect terrain details while removing ground object points.