An approach to improve VI-SLAM based on two-step marginalization and keyframe selection method

被引：0

作者：

Zhang X. ^{[1
]}

Liu Q. ^{[1
]}

Li S. ^{[1
]}

Wang Q. ^{[1
]}

机构：

[1] School of Instrument Science and Engineering, Southeast University, Nanjing

来源：

Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2020年 / 28卷 / 05期

关键词：

Inertial measurement unit; Keyframe selection; SLAM; Two-step marginalization;

D O I：

10.13695/j.cnki.12-1222/o3.2020.05.007

中图分类号：

学科分类号：

摘要：

Aiming at the problem that the mainstream visual-inertial SLAM(VI-SLAM) has a high computational cost in the back-end optimization, a VI-SLAM system based on two-step marginalization and keyframe selection method is proposed. Firstly, in the process of back-end optimization, the factors in the factor graph are classified, and the block matrix corresponding to the factor category in the error optimization equation is marginalized. By decomposing the high-dimensional matrix step by step, the computational efficiency of the system is improved. Secondly, the keyframe selection strategy is improved by increasing the constraint relationship between non-keyframe sliding windows. The proposed algorithm can avoid the feature point tracking failure caused by the large parallax, and improve the stability and average accuracy of the system. Experimental results show that the computational efficiency of our method in the EuRoC dataset is improved by 14.91% compared with the state-of-art SLAM, and the positional accuracy is also improved. © 2020, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.

引用

页码：608 / 614and623

共 19 条

[1]

Zhang X, Zhang Y, Su X., Simultaneous localization and mapping of mobile robot based on heuristic algorithm, Journal of Chinese Inertial Technology, 26, pp. 45-50, (2018)

[2]

Zhang X, Zheng B, Liu Q, Et al., Improving RGB-D SLAM through extracting static region using delaunay triangle mesh, Journal of Chinese Inertial Technology, 27, pp. 661-669, (2019)

[3]

Mur-Artal R., Montiel J.M.M., Tardos J.D., ORB-SLAM: A versatile and accurate monocular SLAM system, IEEE Transactions on Robotics, 31, 5, pp. 1147-1163, (2015)

[4]

Huang G., Visual-Inertial Navigation: A Concise Review. 2019, 2019 International Conference on Robotics and Automation (ICRA), (2019)

[5]

Qin T, Shen S., Robust initialization of monocular visual-inertial estimation on aerial robots, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4225-4232, (2017)

[6]

Forster C., Zhang Z, Gassner M., Et al., SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems, IEEE Transactions on Robotics, 33, 2, pp. 249-265, (2017)

[7]

Qin T, Li P, Shen S., VINS-mono: A robust and versatile monocular visual-inertial state estimator, IEEE Transactions on Robotics, 99, pp. 1-17, (2017)

[8]

Lynen S, Et al., A robust and modular multi-sensor fusion approach applied to mav navagation, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, (2014)

[9]

Mourikis A.I., Roumeliotis S.I., A multi-state constraint Kalman filter for vision-aided inertial navigation, IEEE International Conference on Robotics & Automation, (2007)

[10]

Bloesch M., Omari S., Hutter M., Et al., Robust visual inertial odometry using a direct EKF-based approach, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 298-304, (2015)

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