Aiming at the problem of insufficient segmentation and over-segmentation of 3D lidar in multi-type scene, a lidar ground segmentation method based on the combined features of point cloud clusters is proposed. First, the three-dimensional point cloud is projected into a fan-shaped grid to cluster the connected domains, and the grids with small gradients are clustered into one category. Then, according to the characteristics of the pavement point cloud conforming to the geometric characteristics of the plane and the straight line, the eigenvalue of each cluster is calculated to select the candidate clusters of the pavement grid cluster, and then the gradient in the radial direction is checked to eliminate the misjudged grid. Finally, the cubic B-spline curve is used for smooth fitting to realize the division of ground points and non-ground points. The proposed method is verified in different road conditions. The experimental results show that the accuracy of the proposed method on roads with multiple obstacles is 97.50%, and the calculation time is 27 ms, indicating that the proposed method has higher ground extraction accuracy and stronger road adaptability.