VINS-MKF: A Tightly-Coupled Multi-Keyframe Visual-Inertial Odometry for Accurate and Robust State Estimation
文献类型：期刊论文
| 作者 | Zhang, Chaofan1,2; Liu, Yong1 ; Wang, Fan1,2; Xia, Yingwei1; Zhang, Wen1
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| 刊名 | SENSORS
 |
| 出版日期 | 2018-11-01 |
| 卷号 | 18期号:11页码:28 |
| 关键词 | state estimation visual odometry visual inertial fusion multiple fisheye cameras tightly coupled |
| ISSN号 | 1424-8220 |
| DOI | 10.3390/s18114036 |
| 通讯作者 | Zhang, Chaofan(zcf0413@mail.ustc.edu.cn) ; Zhang, Wen(zhangwen@aiofm.ac.cn) |
| 英文摘要 | State estimation is crucial for robot autonomy, visual odometry (VO) has received significant attention in the robotics field because it can provide accurate state estimation. However, the accuracy and robustness of most existing VO methods are degraded in complex conditions, due to the limited field of view (FOV) of the utilized camera. In this paper, we present a novel tightly-coupled multi-keyframe visual-inertial odometry (called VINS-MKF), which can provide an accurate and robust state estimation for robots in an indoor environment. We first modify the monocular ORBSLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping) to multiple fisheye cameras alongside an inertial measurement unit (IMU) to provide large FOV visual-inertial information. Then, a novel VO framework is proposed to ensure the efficiency of state estimation, by adopting a GPU (Graphics Processing Unit) based feature extraction method and parallelizing the feature extraction thread that is separated from the tracking thread with the mapping thread. Finally, a nonlinear optimization method is formulated for accurate state estimation, which is characterized as being multi-keyframe, tightly-coupled and visual-inertial. In addition, accurate initialization and a novel MultiCol-IMU camera model are coupled to further improve the performance of VINS-MKF. To the best of our knowledge, it's the first tightly-coupled multi-keyframe visual-inertial odometry that joins measurements from multiple fisheye cameras and IMU. The performance of the VINS-MKF was validated by extensive experiments using home-made datasets, and it showed improved accuracy and robustness over the state-of-art VINS-Mono. |
| WOS关键词 | MOTION ; SLAM ; NAVIGATION ; VERSATILE |
| WOS研究方向 | Chemistry ; Electrochemistry ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000451598900446 |
| 出版者 | MDPI |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/40377]  |
| 专题 | 合肥物质科学研究院_应用技术研究所 |
| 通讯作者 | Zhang, Chaofan; Zhang, Wen |
| 作者单位 | 1.Chinese Acad Sci, Inst Appl Technol, Hefei Inst Phys Sci, Hefei 230031, Anhui, Peoples R China 2.Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, Anhui, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Chaofan,Liu, Yong,Wang, Fan,et al. VINS-MKF: A Tightly-Coupled Multi-Keyframe Visual-Inertial Odometry for Accurate and Robust State Estimation[J]. SENSORS,2018,18(11):28. |
| APA | Zhang, Chaofan,Liu, Yong,Wang, Fan,Xia, Yingwei,&Zhang, Wen.(2018).VINS-MKF: A Tightly-Coupled Multi-Keyframe Visual-Inertial Odometry for Accurate and Robust State Estimation.SENSORS,18(11),28. |
| MLA | Zhang, Chaofan,et al."VINS-MKF: A Tightly-Coupled Multi-Keyframe Visual-Inertial Odometry for Accurate and Robust State Estimation".SENSORS 18.11(2018):28. |
入库方式： OAI收割
来源：合肥物质科学研究院
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