The performance of feature matching algorithms is well known to be one of the main Achilles heels of visual SLAM algorithms, and particularly for point-based visual SLAM. Which is prone to fail in low-textured ,enarios like man-made environments where points are insufficient. Yet, many environments in which, despite being low textured, can still reliably estimate line-based geometric primitives. The line-based structural features in the Manhattan world encode useful geometric information of parallelism, orthogonality, and coplanarity in the scene. By fully exploiting these structural features, we propose a novel monocular SLAM system merging feature points and structure lines, which can provide a more accurate estimation of camera poses. To integrate the structure lines into the framework of the system, we have made efforts in the detection, parameterization, feature fusion, and optimization modules of the structure lines. First, we used the consistency of the direction of the structure lines and the vanishing points to extract the structure lines. In the optimization module, we incorporated the error model of the structure lines into the nonlinear optimization framework, and proposed a new optimization strategy. Finally, a complete SLAM system based on points and structure lines is designed. With structure lines as a new observation, the robustness of the matching algorithm between consecutive frames in low-texture scenes is increased, ensuring continuous updating of the tracking thread when the feature points are lost. Secondly, the dominant directions of structure lines can provide global effective constraints to reduce the accumulated orientation errors and the position drift in consequence. Experiments in man-made environments have demonstrated that the proposed system outperforms existing state-of-the-art monocular SLAM systems in terms of accuracy and robustness.

众所周知，特征匹配算法的性能是视觉 SLAM 算法的主要致命弱点之一，尤其是基于点的视觉 SLAM。这在低纹理的环境中很容易失败，例如点不足的人造环境。然而，在许多环境中，尽管纹理很低，但仍然可以可靠地估计基于线的几何图元。曼哈顿世界中基于线的结构特征编码了场景中平行、正交和共面的有用几何信息。通过充分利用这些结构特征，我们提出了一种新的融合特征点和结构线的单目 SLAM 系统，可以提供更准确的相机姿态估计。为了将结构线融入系统框架，我们在检测、结构线的参数化、特征融合和优化模块。首先，我们利用结构线方向和消失点的一致性来提取结构线。在优化模块中，我们将结构线的误差模型纳入非线性优化框架，提出了一种新的优化策略。最后设计了一个完整的基于点和结构线的SLAM系统。以结构线作为新的观察，增加了低纹理场景中连续帧之间匹配算法的鲁棒性，保证了在特征点丢失时跟踪线程的持续更新。其次，结构线的主导方向可以提供全局有效约束，以减少累积的定向误差和由此产生的位置漂移。在人造环境中的实验表明，所提出的系统在准确性和鲁棒性方面优于现有的最先进的单目 SLAM 系统。