Abstract
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 this https URL.
Abstract (translated)
本文探讨了深度学习技术如何通过结合深度特征提取和深度匹配方法来提高基于视觉的SLAM在具有挑战性的环境中的性能。通过结合深度特征提取和深度匹配方法，我们引入了一种多功能的混合视觉SLAM系统，旨在增强在具有挑战性的场景中的适应性，例如低光条件、动态照明、弱纹理区和严重抖动。我们的系统支持多种模式，包括单目、双目、单目-惯性和平面-惯性配置。我们还进行了分析，探讨了如何将视觉SLAM与深度学习方法相结合以启发其他研究者。通过在公开数据集和自采样数据上进行广泛的实验，我们证明了SL-SLAM系统与传统方法相比具有优越性。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。为了造福社区，我们将SL-SLAM的源代码公开在以下链接处：
URL
https://arxiv.org/abs/2405.03413
