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复杂环境中提升基于视觉的SLAM性能。通过融合深度特征提取与深度匹配方法，我们提出了一种通用的混合视觉SLAM系统，旨在增强在低光照、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。该系统支持多种模式，包括单目、双目、单目-惯性和双目-惯性配置。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数据上的广泛实验，我们证明了SL-SLAM系统相较于传统方法的优越性。实验结果表明，SL-SLAM在定位精度和跟踪鲁棒性方面均优于当前最先进的SLAM算法。为惠及社区，我们在https://github.com/zzzzxxxx111/SLslam 公开了源代码。