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Camera-IMU-based localization: Observability analysis and consistency improvement

delete2013-11-13
delete213
PRE
AI
J
Joel A. Hesch *
D
Dimitrios G. Kottas
S
Sean L. Bowman
S
Stergios I. Roumeliotis
DOI:10.1177/0278364913509675delete
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摘要

摘要

En 中文
This work investigates the relationship between system observability properties and estimator inconsistency for a Vision-aided Inertial Navigation System (VINS). In particular, first we introduce a new methodology for determining the unobservable directions of nonlinear systems by factorizing the observability matrix according to the observable and unobservable modes. Subsequently, we apply this method to the VINS nonlinear model and determine its unobservable directions analytically. We leverage our analysis to improve the accuracy and consistency of linearized estimators applied to VINS. Our key findings are evaluated through extensive simulations and experimental validation on real-world data, demonstrating the superior accuracy and consistency of the proposed VINS framework compared to standard approaches.
Keyword:
Vision-aided inertial navigation
visual-inertial odometry
observability analysis
estimator consistency
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期刊

International Journal of Robotics Research 封面图
International Journal of Robotics Research
IF:
5
论文数:
2.4K
被引数:
1.5W

机构

U
University of Minnesota Twin Cities
学者数:
3.7W
论文数: 3.1W
被引数: 58
G
Google Incorporated
学者数:
3.5K
论文数: 1.8K
被引数: 8
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