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技术取得了显著进展，但在低纹理、运动模糊或光照不良等挑战性条件下实现鲁棒运行仍是一个开放性问题。这类场景在视障辅助导航等应用中普遍存在，会严重削弱定位精度与跟踪稳定性，进而降低导航的可靠性与安全性。为突破这些局限，我们提出SELM-SLAM3——一种集成SuperPoint与LightGlue的深度学习增强型视觉SLAM框架，可实现稳健的特征提取与匹配。采用包含多样化挑战场景的TUM RGB-D、ICL-NUIM及TartanAir数据集进行评估，SELM-SLAM3较传统ORB-SLAM3平均性能提升87.84%，并超越现有最优RGB-D SLAM系统36.77%。该框架在低纹理场景与快速运动等复杂条件下表现出优异性能，为开发视障导航辅助设备提供了可靠平台。