This habilitation thesis presents research that aims to enable long-term deployment of
mobile robots in changing environments. The presented approaches encompass methods
that ensure robustness of autonomous visual navigation in outdoor environments for prolonged
time periods, spatio-temporal representations that explicitly model the environment
changes over time, and supporting software modules that enable robust and accurate robot
localisation.
The main contribution of the thesis is a novel approach that allows to incorporate
the notion of time into most stationary environment models used in mobile robotics.
This is achieved by representing the uncertainty of the environment states not by fixed
probabilities, but by probabilistic functions of time, represented in the frequency domain.
The method allows to integrate unlimited numbers of sparse and irregular observations
obtained during long-term deployments of mobile robots into memory-efficient models that
reflect the persistence and recurrence of environment variations. The frequency-enhanced
spatio-temporal models allow to predict the future environment states, which improves the
efficiency of mobile robot operation in changing environments. In this thesis, we present a
series of articles, which demonstrate that the proposed approach improves mobile robot
localization, path and task planning, activity recognition, human-robot interaction and
allows for life-long spatio-temporal exploration of perpetually-changing environments.
en
dc.language.iso
en
en
dc.title
LONG–TERM AUTONOMY OF MOBILE ROBOTS IN CHANGING ENVIRONMENTS
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dc.title.alternative
2018
cze
dc.type
habilitační práce
cze
dc.type
habilitation theses
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theses.degree.grantor
České vysoké učení technické v Praze. Fakulta elektrotechnická. Centrum umělé inteligence