7,339 research outputs found
Observers for invariant systems on Lie groups with biased input measurements and homogeneous outputs
This paper provides a new observer design methodology for invariant systems
whose state evolves on a Lie group with outputs in a collection of related
homogeneous spaces and where the measurement of system input is corrupted by an
unknown constant bias. The key contribution of the paper is to study the
combined state and input bias estimation problem in the general setting of Lie
groups, a question for which only case studies of specific Lie groups are
currently available. We show that any candidate observer (with the same state
space dimension as the observed system) results in non-autonomous error
dynamics, except in the trivial case where the Lie-group is Abelian. This
precludes the application of the standard non-linear observer design
methodologies available in the literature and leads us to propose a new design
methodology based on employing invariant cost functions and general gain
mappings. We provide a rigorous and general stability analysis for the case
where the underlying Lie group allows a faithful matrix representation. We
demonstrate our theory in the example of rigid body pose estimation and show
that the proposed approach unifies two competing pose observers published in
prior literature.Comment: 11 page
State Estimation for Systems on Lie Groups with Nonideal Measurements
This thesis considers the state estimation problem for invariant
systems on Lie groups with inputs in its associated Lie algebra
and outputs in homogeneous spaces of the Lie group. A particular
focus of this thesis is the development of state estimation
methodologies for systems with nonideal measurements, especially
systems with additive input measurement bias, output measurement
delay, and sampled outputs. The main contribution of the thesis
is to effectively employ the symmetries of the system dynamics
and to benefit from the Lie group structure of the underlying
state space in order to design robust state estimators that are
computationally simple and are ideal for embedded applications in
robotic systems.
We address the input measurement bias problem by proposing a
novel nonlinear observer to adaptively eliminate the input
measurement bias. Despite the nonlinear and non-autonomous nature
of the resulting error dynamics and the complexity of the
underlying state space, the proposed observer exhibits
asymptotic/exponential convergence of the state and bias
estimation errors to zero.
To tackle the output measurement delay problem, we propose novel
dynamic predictors used in an observer-predictor arrangement. The
observer provides estimates of the delayed state using the
delayed output measurements and the predictor takes those
estimates, compensates for the delay, and provides predictions of
the current state. Separately, we propose output predictors
employed in a predictor-observer arrangement to address the
problem of sampled output measurements. The output predictors
take the sampled measurements and provide continuous predictions
of the current outputs. Feeding the predicted outputs into the
observer yields estimates of the current state. Both methods rely
on the invariance of the underlying system dynamics to
recursively provide predictions with low computation
requirements.
We demonstrate applications of the theory with examples of
attitude, velocity, and position estimation on SO(3) and SE(3). A
key contribution of this thesis is the development of C++
libraries in an embedded implementation as well as experimental
verification of the developed theory with real flight tests using
model UAVs
Gradient-like observer design on the Special Euclidean group SE(3) with system outputs on the real projective space
A nonlinear observer on the Special Euclidean group for full
pose estimation, that takes the system outputs on the real projective space
directly as inputs, is proposed. The observer derivation is based on a recent
advanced theory on nonlinear observer design. A key advantage with respect to
existing pose observers on is that we can now incorporate in a
unique observer different types of measurements such as vectorial measurements
of known inertial vectors and position measurements of known feature points.
The proposed observer is extended allowing for the compensation of unknown
constant bias present in the velocity measurements. Rigorous stability analyses
are equally provided. Excellent performance of the proposed observers are shown
by means of simulations
Observability, Identifiability and Sensitivity of Vision-Aided Navigation
We analyze the observability of motion estimates from the fusion of visual
and inertial sensors. Because the model contains unknown parameters, such as
sensor biases, the problem is usually cast as a mixed identification/filtering,
and the resulting observability analysis provides a necessary condition for any
algorithm to converge to a unique point estimate. Unfortunately, most models
treat sensor bias rates as noise, independent of other states including biases
themselves, an assumption that is patently violated in practice. When this
assumption is lifted, the resulting model is not observable, and therefore past
analyses cannot be used to conclude that the set of states that are
indistinguishable from the measurements is a singleton. In other words, the
resulting model is not observable. We therefore re-cast the analysis as one of
sensitivity: Rather than attempting to prove that the indistinguishable set is
a singleton, which is not the case, we derive bounds on its volume, as a
function of characteristics of the input and its sufficient excitation. This
provides an explicit characterization of the indistinguishable set that can be
used for analysis and validation purposes
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