  This paper explores how deep learning techniques can improve visual-based
SLAM performance in challenging environments. By combining deep feature
extraction and deep matching methods, we introduce a versatile hybrid visual
SLAM system designed to enhance adaptability in challenging scenarios, such as
low-light conditions, dynamic lighting, weak-texture areas, and severe jitter.
Our system supports multiple modes, including monocular, stereo,
monocular-inertial, and stereo-inertial configurations. We also perform
analysis how to combine visual SLAM with deep learning methods to enlighten
other researches. Through extensive experiments on both public datasets and
self-sampled data, we demonstrate the superiority of the SL-SLAM system over
traditional approaches. The experimental results show that SL-SLAM outperforms
state-of-the-art SLAM algorithms in terms of localization accuracy and tracking
robustness. For the benefit of community, we make public the source code at
https://github.com/zzzzxxxx111/SLslam.
