Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation

TL;DR

This paper introduces SELM-SLAM3, a deep learning-enhanced visual SLAM system that significantly improves robustness and accuracy in challenging conditions, aiding visually impaired navigation.

Contribution

The novel SELM-SLAM3 framework integrates SuperPoint and LightGlue, outperforming traditional SLAM systems in diverse, difficult scenarios for assistive navigation.

Findings

Outperforms ORB-SLAM3 by 87.84% on average

Exceeds state-of-the-art RGB-D SLAM by 36.77%

Demonstrates robustness in low-texture and fast-motion scenarios

Abstract

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.