SLM-VINS: Advancing Visual-Inertial SLAM via Hierarchical Spatial Line Integration and Multi-Mechanism Marginalization

Simultaneous Localization and Mapping (SLAM) has emerged as a cornerstone technology in intelligent transportation systems (ITS) and autonomous robots, finding widespread applications in various scenarios and multiple platforms autonomous driving tasks. However, in environments with weak textures and motion blur, achieving efficient and robust visual-inertial SLAM remains challenging. Current research utilizes line features to enhance SLAM performance in such environments, but incurs difficulties in line extraction, structured scene representation, and computational overheads involved in joint optimization. To address these challenges, this paper introduces a novel SLAM framework with both high pose estimation accuracy and high back-end computational efficiency. Firstly, an intelligent spatial line integration method is proposed to effectively reduce data redundancy in joint optimization by leveraging the remarkable stability of long line segments in structured scenes, thereby enhancing the spatial consistency of structural lines. Secondly, to combat low-light and high-speed motion environments, an optical flow tracking accuracy verification method is designed to bolster the system’s tracking performance and robustness in complex scenarios. Finally, to relieve the substantial computational overhead arising from high-dimensional optimization parameters in bundle adjustment (BA), a multi-mechanism marginalization strategy is presented to enhance the accuracy and computational efficiency of BA, while also preventing scale explosion. Comparative evaluations against state-of-the-art algorithms on both the EuRoC MAV and TUM VI benchmark datasets demonstrate that the proposed framework significantly improves localization accuracy and joint optimization efficiency.