OG-SLAM: A real-time and high-accurate monocular visual SLAM framework

Abstract

The 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 OG-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.