Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Visual SLAM | TUM RGB-D fr1 desk | ATE RMSE (cm)1.9 | 24 | |
| Trajectory Estimation | TUM RGB-D Freiburg1 | RMSE0.019 | 17 | |
| Visual SLAM | TUM RGB-D fr1/room | Translation RMSE (m)0.138 | 8 | |
| Visual SLAM | TUM RGB-D fr1/desk2 | ATE RMSE0.04 | 7 | |
| SLAM | TartanAir Hospital-Hard sequences | ATE (m) P0370.049 | 4 | |
| SLAM | TUM RGB-D Freiburg1 plant | ATE (m)0.034 | 3 | |
| Visual SLAM | ICL-NUIM (various sequences) | ATE (m) - lr-kt00.006 | 3 | |
| SLAM | TUM RGB-D Freiburg1 rpy | ATE (m)0.021 | 2 | |
| SLAM | TUM RGB-D Freiburg1 teddy | ATE (m)0.131 | 2 | |
| SLAM | TUM RGB-D Freiburg1 floor | ATE (m)0.04 | 2 |