{"title":"Real-Time Visual-Inertial Odometry Based on Point-Line Feature Fusion","authors":"G. Yang, W. D. Meng, G. D. Hou, N. N. Feng","doi":"10.1134/s2075108724700068","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>To improve the localization accuracy and tracking robustness of monocular feature-based visual SLAM systems in low-texture environments, a visual-inertial odometry method combining line features and point features is proposed, taking advantage of the easy availability of line features in real-world environments and the high accuracy of feature-based methods. The combination of point and line features ensures accurate positioning of the SLAM system in low-texture environments, while the inclusion of IMU data provides prior information and scale information. The pose is optimized by minimizing the reprojection error of point and line features and the IMU error using bundle adjustment. An improved EDlines algorithm is introduced, which incorporates a pixel chain length suppression process to enhance the effectiveness of extracted line features and reduce the rate of line feature misalignment. Experimental results on the public EuRoC dataset and TUM RGB-D dataset show that the proposed method meets the real-time requirements and has higher localization accuracy and robustness compared with the visual SLAM method based on single point feature or the method adding traditional line features.</p>","PeriodicalId":38999,"journal":{"name":"Gyroscopy and Navigation","volume":"293 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Gyroscopy and Navigation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s2075108724700068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
To improve the localization accuracy and tracking robustness of monocular feature-based visual SLAM systems in low-texture environments, a visual-inertial odometry method combining line features and point features is proposed, taking advantage of the easy availability of line features in real-world environments and the high accuracy of feature-based methods. The combination of point and line features ensures accurate positioning of the SLAM system in low-texture environments, while the inclusion of IMU data provides prior information and scale information. The pose is optimized by minimizing the reprojection error of point and line features and the IMU error using bundle adjustment. An improved EDlines algorithm is introduced, which incorporates a pixel chain length suppression process to enhance the effectiveness of extracted line features and reduce the rate of line feature misalignment. Experimental results on the public EuRoC dataset and TUM RGB-D dataset show that the proposed method meets the real-time requirements and has higher localization accuracy and robustness compared with the visual SLAM method based on single point feature or the method adding traditional line features.
摘要 为了提高基于单目特征的视觉 SLAM 系统在低纹理环境中的定位精度和跟踪鲁棒性，我们提出了一种结合线特征和点特征的视觉惯性里程测量方法，利用了线特征在真实世界环境中的易得性和基于特征方法的高精度。点特征和线特征的结合确保了 SLAM 系统在低纹理环境中的精确定位，而 IMU 数据的加入则提供了先验信息和比例信息。通过最小化点和线特征的重投影误差以及使用捆绑调整的 IMU 误差来优化姿势。此外，还引入了一种改进的 EDlines 算法，其中包含一个像素链长抑制过程，以提高提取线特征的效果并降低线特征错位率。在公开的 EuRoC 数据集和 TUM RGB-D 数据集上的实验结果表明，与基于单点特征的视觉 SLAM 方法或添加传统线特征的方法相比，所提出的方法满足实时性要求，并具有更高的定位精度和鲁棒性。
期刊介绍：
Gyroscopy and Navigation is an international peer reviewed journal that covers the following subjects: inertial sensors, navigation and orientation systems; global satellite navigation systems; integrated INS/GNSS navigation systems; navigation in GNSS-degraded environments and indoor navigation; gravimetric systems and map-aided navigation; hydroacoustic navigation systems; navigation devices and sensors (logs, echo sounders, magnetic compasses); navigation and sonar data processing algorithms. The journal welcomes manuscripts from all countries in the English or Russian language.
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