In the context of autonomous driving in urban environments accurate and reliable information about the vehicle motion is crucial. This article presents a multi-modal sensor fusion scheme that, based on standard production car sensors and an inertial measurement unit, estimates the three-dimensional vehicle velocity and attitude angles (pitch and roll). Moreover, in order to enhance the estimation accuracy, the scheme simultaneously estimates the gyroscope and accelerometer biases. The approach relies on a state-affine representation of a kinematic model with an additional measurement equation based on a single-track model. The sensor fusion scheme is built upon a recently proposed adaptive estimator, which allows a direct consideration of model uncertainties and sensor noise. In order to provide accurate estimates during collision avoidance manoeuvres, a measurement covariance adaptation is introduced, which reduces the influence of the single-track model when its information is superfluous. A validation using experimental data demonstrates the effectiveness of the method during both regular urban drives and collision avoidance manoeuvres.


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    Title :

    Multi-modal sensor fusion for highly accurate vehicle motion state estimation


    Contributors:

    Publication date :

    2020-07-01


    Remarks:

    Marco , V R , Kalkkuhl , J , Raisch , J , Scholte , W , Nijmeijer , H & Seel , T 2020 , ' Multi-modal sensor fusion for highly accurate vehicle motion state estimation ' , Control Engineering Practice , vol. 100 , 104409 . https://doi.org/10.1016/j.conengprac.2020.104409



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629