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

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.