GL-VSLAM: A General Lightweight Visual SLAM Approach for RGB-D and Stereo Cameras

Feature-based indirect SLAM is more robust than direct SLAM; however, feature extraction and descriptor computation are time-consuming. In this paper, we propose GL-VSLAM, a general lightweight visual SLAM approach designed for RGB-D and stereo cameras. GL-VSLAM utilizes sparse optical flow matching based on uniform motion model prediction to establish keypoint correspondences between consecutive frames, rather than relying on descriptor-based feature matching, thereby achieving high real-time performance. To enhance positioning accuracy, we adopt a coarse-to-fine strategy for pose estimation in two stages. In the first stage, the initial camera pose is estimated using RANSAC PnP based on robust keypoint correspondences from sparse optical flow. In the second stage, the camera pose is further refined by minimizing the reprojection error. Keypoints and descriptors are extracted from keyframes for backend optimization and loop closure detection. We evaluate our system on the TUM and KITTI datasets, as well as in a real-world environment, and compare it with several state-of-the-art methods. Experimental results demonstrate that our method achieves comparable positioning accuracy, while its efficiency is up to twice that of ORB-SLAM2.