Abstract:
Unmanned aerial vehicles technologies are getting smaller and cheaper
to use and the challenges of payload limitation in unmanned aerial
vehicles are being overcome. Integrated navigation system design requires
selection of set of sensors and computation power that provides
reliable and accurate navigation parameters (position, velocity
and attitude) with high update rates and bandwidth in small and
cost effective manner. Many of today’s operational unmanned aerial
vehicles navigation systems rely on inertial sensors as a primary measurement
source. Inertial Navigation alone however suffers from slow
divergence with time. This divergence is often compensated for by
employing some additional source of navigation information external
to Inertial Navigation. From the 1990’s to the present day Global
Positioning System has been the dominant navigation aid for Inertial
Navigation. In a number of scenarios, Global Positioning System measurements
may be completely unavailable or they simply may not be
precise (or reliable) enough to be used to adequately update the Inertial
Navigation hence alternative methods have seen great attention.
Aiding Inertial Navigation with vision sensors has been the favoured
solution over the past several years. Inertial and vision sensors with
their complementary characteristics have the potential to answer the
requirements for reliable and accurate navigation parameters.
In this thesis we address Inertial Navigation position divergence. The
information for updating the position comes from combination of vision
and motion. When using such a combination many of the difficulties
of the vision sensors (relative depth, geometry and size of objects,
image blur and etc.) can be circumvented. Motion grants the vision
sensors with many cues that can help better to acquire information
about the environment, for instance creating a precise map of the environment
and localize within the environment.
We propose changes to the Simultaneous Localization and Mapping
augmented state vector in order to take repeated measurements of
the map point. We show that these repeated measurements with certain
manoeuvres (motion) around or by the map point are crucial for
constraining the Inertial Navigation position divergence (bounded estimation
error) while manoeuvring in vicinity of the map point. This
eliminates some of the uncertainty of the map point estimates i.e.
it reduces the covariance of the map points estimates. This concept
brings different parameterization (feature initialisation) of the map
points in Simultaneous Localization and Mapping and we refer to it
as concept of aiding Inertial Navigation by Simultaneous Localization
and Mapping.
We show that making such an integrated navigation system requires
coordination with the guidance and control measurements and the vehicle
task itself for performing the required vehicle manoeuvres (motion)
and achieving better navigation accuracy. This fact brings new
challenges to the practical design of these modern jam proof Global
Positioning System free autonomous navigation systems.
Further to the concept of aiding Inertial Navigation by Simultaneous
Localization and Mapping we have investigated how a bearing only
sensor such as single camera can be used for aiding Inertial Navigation.
The results of the concept of Inertial Navigation aided by
Simultaneous Localization and Mapping were used. New parameterization
of the map point in Bearing Only Simultaneous Localization
and Mapping is proposed. Because of the number of significant problems
that appear when implementing the Extended Kalman Filter in
Inertial Navigation aided by Bearing Only Simultaneous Localization
and Mapping other algorithms such as Iterated Extended Kalman Filter,
Unscented Kalman Filter and Particle Filters were implemented.
From the results obtained, the conclusion can be drawn that the nonlinear
filters should be the choice of estimators for this application.