Scale estimation and refinement in monocular visual-inertial SLAM System

Xufu Mu, Jing Chen*, Zhen Leng, Songnan Lin, Ningsheng Huang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The fusion of monocular visual and inertial cues has become popular in robotics, unmanned vehicle and augmented reality fields. Recent results have shown that optimization-based fusion strategies outperform filtering ones. The visual-inertial ORB-SLAM is optimization-based and has achieved great success. However, it takes all measurements into IMU initialization, which contains outliers, and it lacks of termination criterion. In this paper, we aim to resolve these issues. First, we present an approach to estimate scale, gravity and accelerometer bias together, and regard the estimated gravity as an indication for estimation convergence. Second, we propose a methodology that is able to use weight w derived from the robust norm for outliers handling, so that the estimated scale can be refined. We test our approaches with the public EuRoC datasets. Experimental results show that the proposed methods can achieve good scale estimation and refinement.

源语言英语
主期刊名Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers
编辑Yao Zhao, David Taubman, Xiangwei Kong
出版商Springer Verlag
533-544
页数12
ISBN(印刷版)9783319716060
DOI
出版状态已出版 - 2017
活动9th International Conference on Image and Graphics, ICIG 2017 - Shanghai, 中国
期限: 13 9月 201715 9月 2017

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10666 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议9th International Conference on Image and Graphics, ICIG 2017
国家/地区中国
Shanghai
时期13/09/1715/09/17

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引用此

Mu, X., Chen, J., Leng, Z., Lin, S., & Huang, N. (2017). Scale estimation and refinement in monocular visual-inertial SLAM System. 在 Y. Zhao, D. Taubman, & X. Kong (编辑), Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers (页码 533-544). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 10666 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-71607-7_47