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**Harvard**

Westerlund, A. och Larsson, H. (2015) *Recursive Bayesian Estimation Applied to Autonomous Vehicles*. Göteborg : Chalmers University of Technology (Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, nr: 2015:25).

** BibTeX **

@mastersthesis{

Westerlund2015,

author={Westerlund, Annie and Larsson, Helena Jakobsson},

title={Recursive Bayesian Estimation Applied to Autonomous Vehicles},

abstract={This thesis presents an implementation of a sequential extended Kalman filter applied to position, velocity and
attitude estimation of autonomous vehicles. The filter is self-tuning by the introduction of a particle swarm
otimization which tunes the process noise covariance. The sensor fusion is adaptive through the means of
corrector signals. It accepts correctors in position, velocity and attitude, in all possible combinations. The
algorithm also includes an extended Kalman filter for quaternion update in order to make the estimations
more robust when implementing sensor fusion
exibility. The filter architecture developed in this thesis is
called the adaptive self-tuning extended Kalman filter, or the ASTEK filter. The algorithm was first tested in
MATLAB/Simulink and then implemented and finalized in C++ in order to facilitate real-time performance.
From testings on a truck, the RMS error for estimating position using a GPS corrector lies in the interval
[104; 0:002] m, for velocity in [104; 0:02] m/s, and for the estimated attitude in [103; 0:21] degrees, depending
on the road and driving mode. When using a 2D map corrector, that is correcting for x, y, and yaw, the RMS
estimations of roll and pitch are higher and lying in the interval [1.1, 3.1]. However, it is kept stable as a result
from the quaternion EKF, whereas the z-direction diverges as expected. The results show that the algorithm is
able to produce estimations of high accuracy and that the corrector signals may vary dynamically. Moreover,
the results show how different roads and driving modes in
uence the estimation and error evolution.},

publisher={Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola},

place={Göteborg},

year={2015},

series={Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2015:25},

keywords={autonomous vehicles, sequential extended Kalman filter, ASTEK filter, state estimator, localization, vehicle control, particle swarm optimization, stochastic optimization},

}

** RefWorks **

RT Generic

SR Electronic

ID 223686

A1 Westerlund, Annie

A1 Larsson, Helena Jakobsson

T1 Recursive Bayesian Estimation Applied to Autonomous Vehicles

YR 2015

AB This thesis presents an implementation of a sequential extended Kalman filter applied to position, velocity and
attitude estimation of autonomous vehicles. The filter is self-tuning by the introduction of a particle swarm
otimization which tunes the process noise covariance. The sensor fusion is adaptive through the means of
corrector signals. It accepts correctors in position, velocity and attitude, in all possible combinations. The
algorithm also includes an extended Kalman filter for quaternion update in order to make the estimations
more robust when implementing sensor fusion
exibility. The filter architecture developed in this thesis is
called the adaptive self-tuning extended Kalman filter, or the ASTEK filter. The algorithm was first tested in
MATLAB/Simulink and then implemented and finalized in C++ in order to facilitate real-time performance.
From testings on a truck, the RMS error for estimating position using a GPS corrector lies in the interval
[104; 0:002] m, for velocity in [104; 0:02] m/s, and for the estimated attitude in [103; 0:21] degrees, depending
on the road and driving mode. When using a 2D map corrector, that is correcting for x, y, and yaw, the RMS
estimations of roll and pitch are higher and lying in the interval [1.1, 3.1]. However, it is kept stable as a result
from the quaternion EKF, whereas the z-direction diverges as expected. The results show that the algorithm is
able to produce estimations of high accuracy and that the corrector signals may vary dynamically. Moreover,
the results show how different roads and driving modes in
uence the estimation and error evolution.

PB Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,PB Institutionen för tillämpad mekanik, Fordonsteknik och autonoma system, Chalmers tekniska högskola,

T3 Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, no: 2015:25

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/223686/223686.pdf

OL 30