Vision-based motion estimation for vehicles on test track via cone markers

Fazekas, Máté; Gáspár, Péter; Németh, Balázs

doi:10.1109/coginfocom50765.2020.9237845

Cited by 2 publications

(1 citation statement)

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“…The motion estimation of vehicles can be performed by several methods using a wide range of sensors, such as camera, LiDAR, GNSS (Global Navigation Satellite System), IMU (inertial measurement unit), and wheel encoders. The perception-based methods (Bloesch et al, 2015) demand prior teaching, and well recognizable features are required (Fazekas et al, 2020a). The fusion of GNSS and IMU measurements could be precise in higher velocity scenarios, but the method requires the actual knowledge of the covariances of signals (Caron et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Velocity Estimation via Wheel Circumference Identification

Fazekas

Gáspár

Németh

2021

Period. Polytech. Transp. Eng.

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The article presents a velocity estimation algorithm through the wheel encoder-based odometry and wheel circumference identification. The motivation of the paper is that a proper model can improve the motion estimation in poor sensor performance cases. For example, when the GNSS signals are unavailable, or when the vision-based methods are incorrect due to the insufficient number of features, furthermore, when the IMU-based method fails due to the lack of frequent accelerations. In these situations, the wheel encoders can be an appropriate choice for state estimation. However, this type of estimation suffers from parameter uncertainty. In the paper, a wheel circumference identification is proposed to improve the velocity estimation. The algorithm listens to the incoming sensor measurements and estimates the wheel circumferences recursively with a nonlinear least squares method. The experimental results demonstrate that with the application of the identified parameters in the wheel odometry model, accurate velocity estimation can be obtained with high frequency. Thus, the presented algorithm can improve the motion estimation in the driver assistant functions of autonomous vehicles.

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Section: Introductionmentioning

confidence: 99%