SuperVINS: A Real-Time Visual-Inertial SLAM Framework for Challenging Imaging Conditions

IEEE Senosrs Journal(JSEN)
Hongkun Luo¹ Yang Liu² Chi Guo^3* Zengke Li⁴ Weiwei Song^5
1 Wuhan University ² China University of Mining and Technology
^* represents the corresponding author.   

[Paper]      [Code]    

SuperVINS framework

SuperVINS is a visual-inertial SLAM framework that integrates deep learning features based on VINS-Fusion. It has the characteristics of high accuracy and high robustness.

Abstract

There is an increasing emphasis on achieving high accuracy and robustness in SLAM systems. The traditional visual-inertial SLAM system often struggles with stability under low-light or dynamic illumination, leading to the potential lost of trajectory tracking. High accuracy and robustness are crucial for ensuring the long-term stability and reliable localization performance of SLAM systems. To address the challenges of improving robustness and accuracy in visual-inertial SLAM, this paper proposes SuperVINS, a real-time visual-inertial SLAM framework designed for challenging imaging conditions. In contrast to geometric modeling, deep learning-based features are capable of fully leveraging the implicit information present in images, which is often not captured by geometric features. Therefore, SuperVINS, developed as an enhancement of VINS-Fusion, integrates the deep learning neural network model SuperPoint for feature point extraction and loop closure detection. At the same time, a deep learning neural network LightGlue model for associating feature points is integrated into front-end feature matching. We employed the RANSAC algorithm for matching enhancement to improve robustness against outliers. Additionally, SuperVINS enables flexibly integrated environment-specific SuperPoint bag-of-words models for improved loop closure detection. The system enables real-time localization and mapping. Experimental validation on the well-known EuRoC and UMA-VI datasets demonstrates that SuperVINS achieves comparable accuracy and robustness to other state-of-the-art visual-inertial SLAM systems, particularly in the most challenging sequences. This paper analyzes the advantages of SuperVINS in terms of accuracy, real-time performance, and robustness. To foster knowledge exchange in the field, we have publicly released the code associated with this paper. The code is available at: https://github.com/luohongk/SuperVINS.

Experimental Results

BibTeX

@article{luo2025supervins,
  title={SuperVINS: A Real-Time Visual-Inertial SLAM Framework for Challenging Imaging Conditions},
  author={Luo, Hongkun and Liu, Yang and Guo, Chi and Li, Zengke and Song, Weiwei},
  journal={IEEE Sensors Journal},
  year={2025},
  publisher={IEEE}
}

Acknowledgements: We borrow this template from A-tale-of-two-features.