Monocular visual inertial system based on semi-direct visual odometry

CHENG Jian-lian; HAN Jun; ZHANG Guo-qiang

Chinese

您当前的位置：

首页 >

文章列表页 >

Monocular visual inertial system based on semi-direct visual odometry

更新时间：2026-09-04

- Monocular visual inertial system based on semi-direct visual odometry
- Vol. 38, Issue 4, Pages: 35-39(2021)
- 作者机构：
  
  1. 上海大学通信与信息工程学院,上海,200444
  2. 上海先进通信与数据科学研究院,上海,200444
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
移动端阅览
成坚炼, 韩军, 张国强. 基于半直接法视觉里程计的单目视觉惯性系统[J]. 微电子学与计算机, 2021, 38(4): 35-39.

CHENG Jian-lian, HAN Jun, ZHANG Guo-qiang. Monocular visual inertial system based on semi-direct visual odometry[J]. 2021, 38(4): 35-39.
成坚炼, 韩军, 张国强. 基于半直接法视觉里程计的单目视觉惯性系统[J]. 微电子学与计算机, 2021, 38(4): 35-39. DOI：

CHENG Jian-lian, HAN Jun, ZHANG Guo-qiang. Monocular visual inertial system based on semi-direct visual odometry[J]. 2021, 38(4): 35-39. DOI：

摘要

针对单目视觉系统姿态估计精度不高的问题，提出了一种基于半直接法视觉里程计(SVO)的单目视觉惯性系统.系统使用误差状态卡尔曼滤波器以松耦合方式对视觉信息和惯性导航信息进行融合.视觉前端采用SVO处理图像数据，并修改了SVO的关键帧选择策略以及解决了相机前视场景中由于重定位失败导致姿态估计失败的问题.使用EuRoc数据集中的MH 01子集对所提算法进行验证.实验表明，基于SVO的单目视觉惯性系统能够有效的提高姿态估计精度.相比单目SVO算法，所提算法的位置误差减少了13.4%

旋转角的误差减少了30%.

Abstract

Aiming at the problem of low attitude estimation accuracy of monocular visual system

a monocular visual inertial system based on semi-direct visual odometry (SVO) is proposed. The system uses an error-state Kalman filter to fuse visual information and inertial navigation information in a loosely coupled manner. SVO is used to process image data in the visual front-end

and the SVO keyframe selection strategy is modified to solve the problem of attitude estimation failure due to relocation failure in the front-view scene of the camera. The proposed algorithm is validated by using the MH 01 subset of the EuRoc dataset. The experiment shows that the monocular visual inertial system based on the SVO can effectively improve the accuracy of attitude estimation. Compared with the monocular SVO algorithm

the position error of the proposed algorithm is reduced by 13.4%

the error of the rotation angle is reduced by 30%.

关键词

Keywords

references

XIAO Y, RUAN X G, ZHU X Q, et al. Integrated navigation method of monocular SVO/INS for micro aerial vehicles[J]. Journal of Chinese Inertial Technology, 2019, 27(2): 211-219. DOI: 10.13695/j.cnki.12-1222/o3.2019.02.011.

FORSTER C, CARLONE L, DELLAERT F, et al. On-manifold preintegration for real-time visual-inertial odometry[J]. IEEE Transactions on Robotics, 20 17, 33( 1): 1-2 1. DOI: 10. 1 109/TRO.20 16.259732 1.

TANG L. Design and implementation of semi-direct based monocular visual odometry[D]. Chongqing: Chongqing University, 2018.

SOLÀJ. Quaternion kinematics for the error-state Kalman filter[J]. arXiv: 1711.02508, 2017.

MUR-ARTAL R, TARDO S J D. Visual-inertial monocular SLAM with map reuse[J]. IEEE Robotics and Automation Letters, 2017, 2(2): 796-803. DOI: 10.1109/LRA.2017.2653359.

QIN T, LI P L, SHEN S J. VINS-Mono: a robust and versatile monocular visual-inertial state estimator[J]. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020. DOI: 10.1109/TRO.2018.2853729.

FORSTER C, PIZZOLI M, SCARAMUZZA D. SVO: Fast semi-direct monocular visual odometry[C]//2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China: IEEE, 2014: 15-22. DOI: 10.1109/ICRA.2014.6906584.

YE B. Monocular visual inertial odometry for quadrotor systems[D]. Hanzhou: Zhejiang University, 2017.

CLEMENT L, KELLY J, BARFOOT T D. Monocular visual teach and repeat aided by local ground planarity[M]//WETTERGREEN D S, BARFOOT T D. Field and Service Robotics. Cham: Springer, 2016: 547-561. DOI: 10.1007/978-3-319-27702-8_36.

YANG Z F, SHEN S J. Monocular visual-inertial state estimation with online initialization and camera-IMU extrinsic calibration[J]. IEEE Transactions on Automation Science and Engineering, 2017, 14(1): 39-51. DOI: 10.1109/TASE.2016.2550621.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on the design of blockchain-based AI-generated content traceability framework

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

A review of large model development: technological evolution from BERT to Deepseek

Microservices anomaly detection based on the correlation of monitoring metrics

Monte carlo analysis of V-layout trench SiC neutron detector

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰