Kuang, BoyuChen, YuhengRana, Zeeshan A.2022-08-112022-08-112022-07-26Kuang B, Chen Y, Rana ZA. (2022) OG-SLAM: a real-time and high-accurate monocular visual SLAM framework, Trends in Computer Science and Information Technology, Volume 7, Issue 2, July 2022, pp. 047 - 0542641-3086https://doi.org/10.17352/tcsit.000050https://dspace.lib.cranfield.ac.uk/handle/1826/18315The challenge of improving the accuracy of monocular Simultaneous Localization and Mapping (SLAM) is considered, which widely appears in computer vision, autonomous robotics, and remote sensing. A new framework (ORB-GMS-SLAM (or OG-SLAM)) is proposed, which introduces the region-based motion smoothness into a typical Visual SLAM (V-SLAM) system. The region-based motion smoothness is implemented by integrating the Oriented Fast and Rotated Brief (ORB) features and the Grid-based Motion Statistics (GMS) algorithm into the feature matching process. The OG-SLAM significantly reduces the absolute trajectory error (ATE) on the key-frame trajectory estimation without compromising the real-time performance. This study compares the proposed G-SLAM to an advanced V-SLAM system (ORB-SLAM2). The results indicate the highest accuracy improvement of almost 75% on a typical RGB-D SLAM benchmark. Compared with other ORB-SLAM2 settings (1800 key points), the OG-SLAM improves the accuracy by around 20% without losing performance in real-time. The OG-SLAM framework has a significant advantage over the ORB-SLAM2 system in that it is more robust for rotation, loop-free, and long ground-truth length scenarios. Furthermore, as far as the authors are aware, this framework is the first attempt to integrate the GMS algorithm into the V-SLAM.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Oriented fast and rotated brief featuresGrid-Based Botion Statistics (GMS) algorithmAbsolute Trajectory Error (ATE)Article