  Traditional approaches for Visual Simultaneous Localization and Mapping
(VSLAM) rely on low-level vision information for state estimation, such as
handcrafted local features or the image gradient. While significant progress
has been made through this track, under more challenging configuration for
monocular VSLAM, e.g., varying illumination, the performance of
state-of-the-art systems generally degrades. As a consequence, robustness and
accuracy for monocular VSLAM are still widely concerned. This paper presents a
monocular VSLAM system that fully exploits learnt features for better state
estimation. The proposed system leverages both learnt local features and global
embeddings at different modules of the system: direct camera pose estimation,
inter-frame feature association, and loop closure detection. With a
probabilistic explanation of keypoint prediction, we formulate the camera pose
tracking in a direct manner and parameterize local features with uncertainty
taken into account. To alleviate the quantization effect, we adapt the mapping
module to generate 3D landmarks better to guarantee the system's robustness.
Detecting temporal loop closure via deep global embeddings further improves the
robustness and accuracy of the proposed system. The proposed system is
extensively evaluated on public datasets (Tsukuba, EuRoC, and KITTI), and
compared against the state-of-the-art methods. The competitive performance of
camera pose estimation confirms the effectiveness of our method.
