一种基于深度学习网络的实时、鲁棒且通用的视觉SLAM框架
A real-time, robust and versatile visual-SLAM framework based on deep learning networks
AI总结：
本文提出一种结合深度特征提取与匹配的通用混合视觉SLAM系统，支持单目、双目及惯性配置，在低光照、弱纹理等复杂场景下的定位精度与跟踪鲁棒性优于现有算法，并已开源。
摘要：
本文探讨了深度学习技术如何能在充满挑战的环境中提升基于视觉的SLAM性能。通过结合深度特征提取和深度匹配方法，我们提出了一种通用的混合视觉SLAM系统，旨在增强在低光照条件、动态光照、弱纹理区域和严重抖动等挑战性场景下的适应性。我们的系统支持多种模式，包括单目、双目、单目-惯性以及双目-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集数据上的大量实验，我们展示了SL-SLAM系统相对于传统方法的优越性。实验结果表明，SL-SLAM在定位精度和跟踪鲁棒性方面优于最先进的SLAM算法。为了社区的利益，我们在https://github.com/zzzzxxxx111/SLslam公开了源代码。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
