A real-time, robust and versatile visual-SLAM framework based on deep learning networks
<|reference_start|>A real-time, robust and versatile visual-SLAM framework based on deep learning networks: This paper explores how deep learning techniques can improve visual-based SLAM performance in challenging environments. By combining deep feature extraction and deep matching methods, we introduce a versatile hybrid visual SLAM system designed to enhance adaptability in challenging scenarios, such as low-light conditions, dynamic lighting, weak-texture areas, and severe jitter. Our system supports multiple modes, including monocular, stereo, monocular-inertial, and stereo-inertial configurations. We also perform analysis how to combine visual SLAM with deep learning methods to enlighten other researches. Through extensive experiments on both public datasets and self-sampled data, we demonstrate the superiority of the SL-SLAM system over traditional approaches. The experimental results show that SL-SLAM outperforms state-of-the-art SLAM algorithms in terms of localization accuracy and tracking robustness. For the benefit of community, we make public the source code at https://github.com/zzzzxxxx111/SLslam.<|reference_end|>
@article{xiao2024a,
    title={A real-time, robust and versatile visual-SLAM framework based on deep
  learning networks},
    author={Zhang Xiao, Shuaixin Li},
    journal={arXiv preprint arXiv:2405.03413},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03413},
    primaryClass={cs.RO}
}