This letter presents a hybrid framework, both in front-end and back-end, for monocular simultaneous localization and mapping (SLAM), capable of utilizing the robustness of feature matching and the accuracy of direct alignment. In the front-end, the feature-based method is first used for coarse pose estimation that is subsequently taken by the direct alignment module as initialization for further refinement. In the back-end, a double window structure is constructed based on the maintained semi-dense map and the sparse feature map, of which the states are optimized via a multi-layer optimization scheme based on the reprojection constraints and the relative pose constraints. Our evaluation on several public datasets demonstrates that this hybrid design retains the superior resilience to scene variations of salient features, and achieves better tracking accuracy due to the integration of the direct modules, leading to a comparable performance with the state-of-the-arts.

中文翻译：

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使用双窗口优化的混合单眼SLAM

这封信提出了用于单眼同时定位和制图（SLAM）的前端和后端的混合框架，该框架能够利用特征匹配的鲁棒性和直接对齐的准确性。在前端，基于特征的方法首先用于粗略姿态估计，随后直接对准模块将其用作初始化以进行进一步的细化。在后端，基于维护的半密集图和稀疏特征图构造双窗口结构，其中状态基于重投影约束和相对姿态约束通过多层优化方案进行优化。我们对多个公开数据集的评估表明，这种混合设计保留了对显着特征场景变化的出色复原力，