Real-Time Offset Error Compensation of 6D IMU Mounted on Ground Vehicles Using Disturbance Observer

Oh, Joa Sub; Noh, Yoon-Kab; Choi, Seibum B.

doi:10.7763/jacn.2013.v1.18

Cited by 4 publications

(2 citation statements)

References 10 publications

(5 reference statements)

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“…Starting from [11], a heuristic correction term was added in [19] to bring the estimated lateral speed to zero when the vehicle is driving straight. The lateral speed estimation in [20] and [21] was improved by utilizing the measurement bias estimates of an inertial measurement unit (IMU). Extended KFs were implemented in [22], [23], and [24] using the two-and three-dimensional kinematic models.…”

Section: B Related Workmentioning

confidence: 99%

Robust and Sample-Efficient Estimation of Vehicle Lateral Velocity Using Neural Networks With Explainable Structure Informed by Kinematic Principles

Lio,

Piccinini,

Biral

2023

IEEE Trans. Intell. Transport. Syst.

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This paper presents kinematics-structured neural networks (KS-NN) for the lateral speed estimation of vehicles. The internal structure of the networks is designed to incorporate the kinematic principles, enhancing the physical explainability and generalization capacity. Both the internal structure and training method are devised for better generalization performance. Various linear and nonlinear variants of our estimator are assessed for accuracy and robustness. The approach is validated using an openly accessible dataset with two race cars. The performance of the novel networks is evaluated against Luenberger, neural network, factor graph and Kalman filter estimators from the literature. In comparison with a Luenberger kinematic observer, our networks improve noise rejection, and overcome the well-known observability problem for low yaw rates. Compared to existing neural network estimators, our networks exhibit better generalization capacity, are more sampleefficient, require fewer learnable parameters, and their structure is physically explainable.

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Section: B Related Workmentioning

confidence: 99%

Robust and Sample-Efficient Estimation of Vehicle Lateral Velocity Using Neural Networks With Explainable Structure Informed by Kinematic Principles

Lio,

Piccinini,

Biral

2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…A well known nonlinear vehicle state observer was first introduced in [18] and proved to be asymptotically stable for all cornering conditions (non-zero yaw rate). The method is later strengthen by an online sensor bias estimation in [37,19]. A similar method, with a more advance Extended Kalman Filter (EKF), is presented in [49].…”

Section: Introductionmentioning

confidence: 99%