The extraction of a digital elevation model (DEM) from airborne lidar point clouds is an important task in the field of geoinformatics. In this paper, we describe a new automated scheme that utilizes the so-called "climbingand-sliding" method to search for ground points from lidar point clouds for DEM generation. The new method has the capability of performing a local search while preserving the merits of a global treatment. This is done by emulating the natural movements of climbing and sliding in order to search for ground points on a terrain surface model. To improve efficiency and accuracy, the scheme is implemented with a pseudo-grid data and includes a back selection step for densification. The test data include a dataset released from the ISPRS Working Group III/3 and one for a mountainous area located in southern Taiwan. The experimental results indicate that the proposed method is capable at producing a high fidelity terrain model.