In this article, we propose enhancements to VINS-Fusion by incorporating deep learning features and deep learning matching methods. We implemented the training of deep learning feature bag of words and utilized these features for loop closure detection. Additionally, we introduce the RANSAC algorithm in the deep learning feature matching module to optimize matching. SuperVINS, an improved version of VINS-Fusion, outperforms it in terms of positioning accuracy, robustness, and more. Particularly in challenging scenarios like low illumination and rapid jitter, traditional geometric features fail to fully exploit image information, whereas deep learning features excel at capturing image features.To validate our proposed improvement scheme, we conducted experiments using open source datasets. We performed a comprehensive analysis of the experimental results from both qualitative and quantitative perspectives. The results demonstrate the feasibility and effectiveness of this deep learning-based approach for SLAM systems.To foster knowledge exchange in this field, we have made the code for this article publicly available. You can find the code at this link: https://github.com/luohongk/SuperVINS.

翻译：本文提出了一种通过融合深度学习特征与深度学习匹配方法来增强VINS-Fusion的框架。我们实现了基于深度学习特征的词袋模型训练，并利用这些特征进行回环检测。此外，我们在深度学习特征匹配模块中引入了RANSAC算法以优化匹配效果。作为VINS-Fusion的改进版本，SuperVINS在定位精度、鲁棒性等方面均优于原系统。特别是在低光照与快速抖动等挑战性场景中，传统几何特征难以充分利用图像信息，而深度学习特征则能更有效地提取图像特征。为验证所提改进方案的有效性，我们基于开源数据集进行了实验，并从定性与定量两个角度对实验结果进行了全面分析。结果表明，这种基于深度学习的方法对于SLAM系统具有可行性与有效性。为促进该领域的知识交流，本文代码已公开，访问链接为：https://github.com/luohongk/SuperVINS。