SuperVINS: A Real-Time Visual-Inertial SLAM Framework for Challenging Imaging Conditions
Hongkun Luo, Yang Liu, Chi Guo, Zengke Li, Weiwei Song
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.