Zhao Wei, Sun Hongyang, Ma Songsong, Wang Haitao
School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China.
School of Mechanical, Electronic & Information Engineering, Shandong University, Weihai 264209, China.
Sensors (Basel). 2025 Jun 20;25(13):3837. doi: 10.3390/s25133837.
This paper presents SE2-LET-VINS, an enhanced Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) system built upon the classic Visual-Inertial Navigation System for Monocular Cameras (VINS-Mono) framework, designed to improve localization accuracy and robustness in complex environments. By integrating Lightweight Neural Network (LET-NET) for high-quality feature extraction and Special Euclidean Group in 2D (SE2) optical flow tracking, the system achieves superior performance in challenging scenarios such as low lighting and rapid motion. The proposed method processes Inertial Measurement Unit (IMU) data and camera data, utilizing pre-integration and RANdom SAmple Consensus (RANSAC) for precise feature matching. Experimental results on the European Robotics Challenges (EuRoc) dataset demonstrate that the proposed hybrid method improves localization accuracy by up to 43.89% compared to the classic VINS-Mono model in sequences with loop closure detection. In no-loop scenarios, the method also achieves error reductions of 29.7%, 21.8%, and 24.1% on the MH_04, MH_05, and V2_03 sequences, respectively. Trajectory visualization and Gaussian fitting analysis further confirm the system's good robustness and accuracy. SE2-LET-VINS offers a robust solution for visual-inertial navigation, particularly in demanding environments, and paves the way for future real-time applications and extended capabilities.
本文介绍了SE2-LET-VINS，这是一种基于经典单目相机视觉惯性导航系统（VINS-Mono）框架构建的增强型视觉惯性同步定位与建图（VI-SLAM）系统，旨在提高复杂环境中的定位精度和鲁棒性。通过集成用于高质量特征提取的轻量级神经网络（LET-NET）和二维特殊欧几里得群（SE2）光流跟踪，该系统在低光照和快速运动等具有挑战性的场景中实现了卓越的性能。所提出的方法处理惯性测量单元（IMU）数据和相机数据，利用预积分和随机抽样一致性（RANSAC）进行精确的特征匹配。在欧洲机器人挑战赛（EuRoc）数据集上的实验结果表明，在具有闭环检测的序列中，与经典的VINS-Mono模型相比，所提出的混合方法将定位精度提高了43.89%。在无环场景中，该方法在MH_04、MH_05和V2_03序列上分别实现了29.7%、21.8%和24.1%的误差降低。轨迹可视化和高斯拟合分析进一步证实了该系统具有良好的鲁棒性和准确性。SE2-LET-VINS为视觉惯性导航提供了一种强大的解决方案，特别是在苛刻的环境中，并为未来的实时应用和扩展功能铺平了道路。
