泡泡一分钟：FMD Stereo SLAM: Fusing MVG and Direct Formulation Towards Accurate and Fast Stereo SLAM

FMD Stereo SLAM: Fusing MVG and Direct Formulation Towards Accurate and Fast Stereo SLAM

FMD Stereo SLAM：融合MVG和直接方法，实现准确，快速的双目SLAM

Fulin Tang, Heping Li, Yihong Wu

We propose a novel stereo visual SLAM framework considering both accuracy and speed at the same time. The framework makes full use of the advantages of key-feature based multiple view geometry(MVG)and direct-based formulation. At the front-end, our system performs direct formulation and constant motion model to predict a robust initial pose, reprojects local map to find 3D-2D correspondence and finally refines pose by the reprojection error minimization. This frontend process makes our system faster. At the back-end, MVG is used to estimate 3D structure. When a new keyframe is inserted, new mappoints are generated by triangulating. In order to improve the accuracy of the proposed system, bad mappoints are removed and a global map is kept by bundle adjustment. Especially, the stereo constraint is performed to optimize the map. This back-end process makes our system more accurate. Experimental evaluation on EuRoC dataset shows that the proposed algorithm can run at more than 100 frames per second on a consumer computer while achieving highly competitive accuracy.

我们提出了一种新颖的双目视觉SLAM框架，同时兼顾了精度和速度。该框架充分利用了基于关键特征的多视图几何（MVG）和基于直接方法的优势。在前端，我们的系统执行直接公式和恒定运动模型来预测稳健的初始姿势，重新投影局部地图以找到3D-2D对应，并通过重投影误差最小化来最终确定姿势。这个前端过程使我们的系统更快。 在后端，MVG用于估计3D结构。 插入新关键帧时，通过三角测量生成新的地图点。为了提高所提出系统的准确性，删除了坏的地图点并通过束调整保持全局地图。 特别地，执行立体约束以优化地图。这种后端流程使我们的系统更加准确。 对EuRoC数据集的实验评估表明，所提出的算法可以在消费者计算机上以每秒100帧以上的速度运行，同时实现高度竞争的准确性。