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Asynchronous sensor fusion of GPS, IMU and CAN-based odometry for heavy-duty vehicles

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Asynchronous sensor fusion of GPS, IMU and CAN-based odometry for heavy-duty vehicles

dc.contributor.author	Girbés, Vicent
dc.contributor.author	Armesto, Leopoldo
dc.contributor.author	Hernández Ferrándiz, Daniel
dc.contributor.author	Dols, Juan F.
dc.contributor.author	Sala, Antonio
dc.date.accessioned	2022-04-27T13:38:05Z
dc.date.available	2022-04-27T13:38:05Z
dc.date.issued	2021
dc.identifier.citation	Girbés, Vicent Armesto, Leopoldo Hernández Ferrándiz, Daniel Dols, Juan F. Sala, Antonio 2021 Asynchronous sensor fusion of GPS, IMU and CAN-based odometry for heavy-duty vehicles IEEE Transactions on Vehicular Technology 70 9 8617 8626
dc.identifier.uri	https://hdl.handle.net/10550/82434
dc.description.abstract	In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear and angular speed readings from wheel tachometers on the internal Controller Area Network (CAN). These signals have different noise variance, bandwidth and sampling rate (being the latter, possibly, irregular). In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing. It is applied to achieve accuracy improvements when incorporating odometry measurements from CAN bus to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that this asynchronous multi-sensor (GPS+IMU+CAN-based odometry) fusion is advantageous in low-speed manoeuvres, improving accuracy and robustness to missing data, thanks to non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother, with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants; it can provide data to subsequent processing steps at arbitrary time instants, not necessarily coincident with the original measurement ones. Given the extra information available in the smoothing case, its estimation performance is less sensitive to the noise-variance parameter setting, compared to causal filtering. Working Matlab code is provided at the end of this work.
dc.language.iso	eng
dc.relation.ispartof	IEEE Transactions on Vehicular Technology, 2021, vol. 70, num. 9, p. 8617-8626
dc.subject	Vehicles
dc.subject	Models matemàtics
dc.subject	Processos estocàstics
dc.title	Asynchronous sensor fusion of GPS, IMU and CAN-based odometry for heavy-duty vehicles
dc.type	journal article	es_ES
dc.date.updated	2022-04-27T13:38:06Z
dc.identifier.doi	10.1109/TVT.2021.3101515
dc.identifier.idgrec	151931
dc.rights.accessRights	open access	es_ES