期刊
SENSORS
卷 21, 期 16, 页码 -出版社
MDPI
DOI: 10.3390/s21165409
关键词
vehicle dynamics estimation; sideslip angle estimation; factor graph; graphical models; Kalman filtering
资金
- project Agricultural Interoperability and Analysis System (ATLAS), H2020 [857125]
- project Multimodal Sensing for Individual Plant Phenotyping in Agriculture Robotics (ANTONIO), ICT-AGRI-FOODCOFUND [41946]
The study proposed a novel approach to model vehicle dynamics directly as a graphical model, which can accurately estimate and monitor sideslip angle, with a flexible mathematical framework and greater potential for future extensions.
Sideslip angle is an important variable for understanding and monitoring vehicle dynamics, but there is currently no inexpensive method for its direct measurement. Therefore, it is typically estimated from proprioceptive sensors onboard using filtering methods from the family of the Kalman filter. As a novel alternative, this work proposes modeling the problem directly as a graphical model (factor graph), which can then be optimized using a variety of methods, such as whole-dataset batch optimization for offline processing or fixed-lag smoothing for on-line operation. Experimental results on real vehicle datasets validate the proposal, demonstrating a good agreement between estimated and actual sideslip angle, showing similar performance to state-of-the-art methods but with a greater potential for future extensions due to the more flexible mathematical framework. An open-source implementation of the proposed framework has been made available online.
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