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@PHDTHESIS{Zhang:1026564,
author = {Zhang, Haoming},
othercontributors = {Vallery, Heike and Barfoot, Timothy and Kok, Manon},
title = {{H}andling {GNSS}-outliers in multisensor state estimation},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2026-00971},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2026; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {Vehicle localization using classic state estimation
approaches generally conducts least-square techniques to
infer unknown state variables by fusing nonlinear system
dynamics and sensor models. In this setting, model
uncertainties are typically assumed to follow a zero-mean
Gaussian distribution. An optimal state estimate can be
achieved by minimizing covariances using redundant state
representations (aka models), provided the predefined noise
parameters match the actual uncertainties. However, the
assumption of Gaussian noise has been shown to be overly
conservative and unrealistic in real-world environments,
where sensor failures and signal interference frequently
result in measurement outliers. In this case, measurement
outliers typically exhibit time-varying noise dynamics
associated with heavy-tailed, skewed, and multimodal
distributions that cannot be adequately modeled in advance.
Consequently, a state estimator employing the least-square
technique may degrade or even completely diverge once an
inconsistent measurement model with overly confident
hand-crafted noise parameters is used. Therefore, developing
a vehicle localization approach that achieves robust
long-term operation in diverse environments remains a
challenging problem. This dissertation examines methods for
handling outliers in a graph-optimization-based state
estimator, specifically designed for vehicle localization
using Global Navigation Satellite Systems (GNSS). In
addition to developing novel state estimation techniques,
this thesis places noticeable emphasis on two key areas: (a)
a theoretical analysis of the state-of-the-art robust
outlier rejection approaches, and (b) learning-based
methods, supported by extensive ablation studies. In this
regard, I explore two methods for handling GNSS outliers.
The first considers the classic M-estimation techniques,
which are commonly used to reduce the impact of outliers in
state estimation and have proven effective in practice.
However, their application to ranging sensors, particularly
in terms of robustness and efficiency across different loss
kernels, has received little attention. The second method
leverages learning-based techniques to predict outlier
measurements. Beyond traditional machine learning models,
such as Support Vector Machines, I introduce a novel deep
network architecture that performs spatiotemporal modeling,
specifically considering the characteristics of GNSS noise
propagation. In summary, this dissertation advances the
understanding of outlier handling using M-estimation and
learning-based methods to enhance the robustness of vehicle
localization for long-term operation in challenging
environments. By emphasizing these approaches, the thesis
aims to provide critical insights and inspire future
advancements in robust state estimation for vehicle
localization. Additionally, I prioritize reproducibility and
comparability by open-sourcing the code for all methods
discussed.},
cin = {416610},
ddc = {620},
cid = {$I:(DE-82)416610_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2026-00971},
url = {https://publications.rwth-aachen.de/record/1026564},
}
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