  Simultaneous Localization and Mapping (SLAM) has become a critical technology
for intelligent transportation systems and autonomous robots and is widely used
in autonomous driving. However, traditional manual feature-based methods in
challenging lighting environments make it difficult to ensure robustness and
accuracy. Some deep learning-based methods show potential but still have
significant drawbacks. To address this problem, we propose a novel hybrid
system for visual SLAM based on the LightGlue deep learning network. It uses
deep local feature descriptors to replace traditional hand-crafted features and
a more efficient and accurate deep network to achieve fast and precise feature
matching. Thus, we use the robustness of deep learning to improve the whole
system. We have combined traditional geometry-based approaches to introduce a
complete visual SLAM system for monocular, binocular, and RGB-D sensors. We
thoroughly tested the proposed system on four public datasets: KITTI, EuRoC,
TUM, and 4Season, as well as on actual campus scenes. The experimental results
show that the proposed method exhibits better accuracy and robustness in
adapting to low-light and strongly light-varying environments than traditional
manual features and deep learning-based methods. It can also run on GPU in real
time.
