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

Marziyeh Bamdad, Marziyeh Bamdad, Hans-Peter Hutter, Alireza Darvishy

2025

Abstract

Despite advancements in SLAM technologies, robust operation under challenging condition 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.

Paper Citation

in Harvard Style

Bamdad M., Hutter H. and Darvishy A. (2025). Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 758-765. DOI: 10.5220/0013338200003912

in Bibtex Style

@conference{visapp25,
author={Marziyeh Bamdad and Hans-Peter Hutter and Alireza Darvishy},
title={Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={758-765},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013338200003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation
SN - 978-989-758-728-3
AU - Bamdad M.
AU - Hutter H.
AU - Darvishy A.
PY - 2025
SP - 758
EP - 765
DO - 10.5220/0013338200003912
PB - SciTePress