Semi-direct monocular visual and visual-inertial SLAM with loop closure detection

Abstract A novel semi-direct monocular visual simultaneous localization and mapping (SLAM) system is proposed to maintain the fast performance of a direct method and the high precision and loop closure capability of a feature-based method. This system extracts and matches Oriented FAST and Rotated BRIEF features in a keyframe and tracks a non-keyframe via a direct method without the requirement of extracting and matching features. A keyframe is used for global or local optimization and loop closure, whereas a non-keyframe is used for fast tracking and localization, thereby combining the advantages of direct and feature-based methods. A monocular visual-inertial SLAM system that fuses inertial measurement data with visual SLAM is also proposed. This system successfully recovers the metric scale successfully. The evaluation on datasets shows that the proposed approach accomplishes loop closure detection successfully and requires less time to achieve accuracy comparable with that of feature-based method. The physical experiment demonstrates the feasibility and robustness of the proposed SLAM. The approach achieves good balance between speed and accuracy and provides valuable references for design and improvement of other SLAM methods.