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.

翻译：本文探讨了深度学习技术如何在具有挑战性的环境下提升基于视觉的SLAM性能。通过结合深度特征提取与深度匹配方法，我们提出了一种通用混合视觉SLAM系统，旨在增强在低光照条件、动态光照、弱纹理区域及剧烈抖动等挑战性场景中的适应性。该系统支持单目、双目、单目惯性及双目惯性等多种模式。我们还分析了如何将视觉SLAM与深度学习方法相结合，以启发其他研究。通过在公开数据集和自采样数据上的大量实验，我们证明了SL-SLAM系统相对于传统方法的优越性。实验结果表明，SL-SLAM在定位精度与跟踪鲁棒性方面优于当前最先进的SLAM算法。为惠及社区，我们在https://github.com/zzzzxxxx111/SLslam上公开了源代码。