Despite advancements in SLAM technologies, robust operation under challenging conditions such as low-texture, motion-blur, or challenging lighting remains an open challenge. Such conditions are common in applications such as assistive navigation for the visually impaired. These challenges undermine localization accuracy and tracking stability, reducing navigation reliability and safety. To overcome these limitations, we present SELM-SLAM3, a deep learning-enhanced visual SLAM framework that integrates SuperPoint and LightGlue for robust feature extraction and matching. We evaluated our framework using TUM RGB-D, ICL-NUIM, and TartanAir datasets, which feature diverse and challenging scenarios. SELM-SLAM3 outperforms conventional ORB-SLAM3 by an average of 87.84% and exceeds state-of-the-art RGB-D SLAM systems by 36.77%. Our framework demonstrates enhanced performance under challenging conditions, such as low-texture scenes and fast motion, providing a reliable platform for developing navigation aids for the visually impaired.

翻译：尽管SLAM技术已取得显著进展，但在低纹理、运动模糊或复杂光照等挑战性条件下的鲁棒运行仍是一个开放性问题。这些条件在视障辅助导航等应用中十分常见。上述挑战会削弱定位精度与跟踪稳定性，从而降低导航的可靠性与安全性。为克服这些局限，我们提出了SELM-SLAM3——一种集成SuperPoint与LightGlue以实现鲁棒特征提取与匹配的深度学习增强型视觉SLAM框架。我们使用包含多样化挑战性场景的TUM RGB-D、ICL-NUIM和TartanAir数据集对该框架进行了评估。SELM-SLAM3的平均性能较传统ORB-SLAM3提升87.84%，并超越当前最先进的RGB-D SLAM系统36.77%。我们的框架在低纹理场景与快速运动等挑战性条件下表现出增强的性能，为开发视障导航辅助工具提供了可靠平台。