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.

翻译：基于平面特征的视觉SLAM（同步定位与地图构建）已在环境结构感知、增强现实等领域获得广泛应用。然而现有研究在平面模糊场景中面临准确定位与地图构建的挑战，主要源于所采用的平面特征及数据关联方法精度不足。本文提出一种面向平面模糊场景的平面特征视觉SLAM系统，涵盖平面处理、数据关联及多约束因子图优化三个模块。我们引入融合语义信息与平面特征的平面处理策略，提取平面的边缘与顶点，并应用于平面选择、数据关联及位姿优化等任务。接着，提出一种综合平面参数、语义信息、投影交并比及非参数检验的数据关联策略，在平面模糊场景中实现精确鲁棒的平面数据关联。最后，设计了一套用于相机位姿优化的多约束因子图。在公开数据集上的定性与定量实验表明，与现有最优方法相比，本系统在地图构建和相机定位的精度与鲁棒性方面均具备竞争力。