基于深度学习的视觉SLAM：辅助视障导航
Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation
AI总结：
本文提出SELM-SLAM3框架，结合SuperPoint和LightGlue提升特征提取与匹配鲁棒性，在低纹理、运动模糊等挑战性条件下实现更精确的定位与跟踪，提升视障导航可靠性。
摘要：
尽管SLAM技术有所进步，但在低纹理、运动模糊或恶劣光照等挑战性条件下仍存在鲁棒性不足的问题。这些条件在视障导航等应用中普遍存在，影响定位精度和跟踪稳定性。为解决这些问题，我们提出了SELM-SLAM3，一种结合SuperPoint和LightGlue的深度学习增强视觉SLAM框架。通过在TUM RGB-D、ICL-NUIM和TartanAir数据集上的评估，SELM-SLAM3在平均性能上比传统ORB-SLAM3高87.84%，优于现有最先进RGB-D SLAM系统36.77%。该框架在低纹理场景和高速运动等挑战性条件下表现出色，为开发视障导航辅助系统提供了可靠的平台。
英文摘要：
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.
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。
