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.

提出一种基于LightGlue深度学习网络的视觉SLAM系统，用于解决传统基于手工特征和深度学习方法在低光和强光变化环境下鲁棒性和准确性不足的问题。在KITTI、EuRoC、TUM和4Season四个公开数据集以及实际校园场景中全面测试，实验结果表明该方法在适应低光和强光变化环境中比传统手工特征和深度学习方法具有更好的准确性和鲁棒性，并且能够实时在GPU上运行。

Light-SLAM：基于LightGlue的鲁棒深度学习视觉SLAM系统在挑战性光照条件下的应用