Currently, the application of ground surface extraction technology in 3D point clouds has attracted extensive research attention. Such technology is widely used in the environmental perception and local navigation functions of Unmanned Ground Vehicles (UGVs). However, due to the heterogeneous density and unstructured spatial distribution of point clouds, the computational time and space complexity is relatively high in ground detection. In addition, since light detection and ranging (LiDAR) sensing can obtain more than 700,000 points per second, ground point clouds require more memory, compared to non-ground point clouds. Thus, ground point clouds extraction is a vitally important function for UGVs to realize intelligent driving. To extract precise ground information, this paper proposes a 3D Hough transform (3DHT) algorithm for ground detection from 3D LiDAR point clouds. We create a special Hough space within the walking slope range and transform all of the 3D points into this 3D Hough space according to the proposed 3DHT algorithm. Then, the maximal peak is extracted and ground equation parameters are obtained using the inverse Hough transform. To reduce the time required, we apply a Graphics Processing Unit (GPU) parallel computation method to reduce the number of typically exhaustive iterations.