Data fusion for localization using state estimation and machine learning
Typ dokumentu
disertační práceAutor
Šimánek, Jakub
Vedoucí práce
Roháč, Jan
Reinštein, Michal
Studijní obor
Provoz a řízení letecké dopravyStudijní program
Elektrotechnika a informatikaInstituce přidělující hodnost
České vysoké učení technické v Praze. Fakulta elektrotechnická. Katedra měření.Metadata
Zobrazit celý záznamAbstrakt
Using multiple sensory information is acknowledged as one of the major topics in the navigation
of aerial and ground vehicles. This doctoral thesis considers localization as a state
estimation problem, which is solved by data fusion techniques and supported by machine
learning methods. It attempts to address the issue of developing a better localization
system for a ground vehicle by seeking the best possible pose estimator (i.e., position,
velocity and attitude) and improving its robustness to unexpected sensor measurements.
The vehicle of interest is represented by a skid-steer tracked mobile robot; however, all
the algorithms work with a sensory set, which can be with minor changes deployed on any
vehicle, legged, wheeled, tracked, or aerial. First part of this thesis explores the development
of different state estimation architectures, which exploit the extended Kalman filter
for full 3D dead reckoning (i.e., incremental or relative pose estimation). The purpose of
this part is to use inertial and odometry dead reckoning to its optimal extent, considering
both the performance and computational complexity. Such combination of proprioceptive
sensory modalities used on a ground vehicle is expected to provide the core localization—
foundation for any other higher level localization or navigation systems. Second part of
the dissertation investigates means of improving overall robustness and performance of the
multi-modal state estimation. Different sensory modalities are prone to various types of
errors, especially in an environment that changes dynamically. Therefore, the thesis shows
the importance of identifying and rejecting unexpected or erroneous measurements. The
multi-modal data fusion is based on inertial and odometry measurements aided by information
from a camera and laser range finder. These two exteroceptive modalities are in
particular prone to real-world disturbances, therefore, they are the subject of anomaly detection
process. Various state-of-the-art machine learning methods (i.e., logistic regression,
Support Vector Machines, Gaussian Mixture Models and Gaussian Processes) are applied
in a Kalman filter framework to monitor the measurements and overcome the commonly
used covariance monitoring and chi-squared gating test. Verification of all the techniques
in this thesis is supported by extensive experimental datasets collected with a real mobile
robot in both indoor and challenging outdoor environments.
Kolekce
- Disertační práce - 13000 [721]
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