  Visual SLAM (Simultaneous Localization and Mapping) based on planar features
has found widespread applications in fields such as environmental structure
perception and augmented reality. However, current research faces challenges in
accurately localizing and mapping in planar ambiguous scenes, primarily due to
the poor accuracy of the employed planar features and data association methods.
In this paper, we propose a visual SLAM system based on planar features
designed for planar ambiguous scenes, encompassing planar processing, data
association, and multi-constraint factor graph optimization. We introduce a
planar processing strategy that integrates semantic information with planar
features, extracting the edges and vertices of planes to be utilized in tasks
such as plane selection, data association, and pose optimization. Next, we
present an integrated data association strategy that combines plane parameters,
semantic information, projection IoU (Intersection over Union), and
non-parametric tests, achieving accurate and robust plane data association in
planar ambiguous scenes. Finally, we design a set of multi-constraint factor
graphs for camera pose optimization. Qualitative and quantitative experiments
conducted on publicly available datasets demonstrate that our proposed system
competes effectively in both accuracy and robustness in terms of map
construction and camera localization compared to state-of-the-art methods.
