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

Fulin Tang, Heping Li, Yihong Wu

Abstract:

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-featurebased
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.