In visual simultaneous localization and mapping (SLAM), ground information not only provides a reference for gravity direction but also effectively aids in obstacle recognition, making accurate detection of the ground plane crucial for robot navigation. To address the problem of ground plane estimation in monocular visual SLAM with limited computational resources and lacking depth information, this paper proposes a ground detection method based on homography. Initially, the homography matrix is computed using the RANSAC method on matched feature point pairs in the initial environment, obtaining the initial ground plane and corresponding ground point cloud. Subsequently, based on the obtained ground seed points, the homography estimation is combined with a dynamic growth strategy during the SLAM mapping process to gradually expand the ground point cloud, achieving precise segmentation of the ground plane at a low computational cost. Experimental results show that the proposed method achieves segmentation accuracy exceeding 92.52% on public datasets and local test data, with an angular error of less than 0.13° for the ground plane and a normalized plane distance error of less than 0.008, validating the effectiveness of the method. Additionally, the proposed algorithm only increases computational cost by 4.57%, meeting real-time operation requirements.