405 research outputs found
Geometric State Observers for Autonomous Navigation Systems
The development of reliable state estimation algorithms for autonomous navigation systems is of great interest in the control and robotics communities. This thesis studies the state estimation problem for autonomous navigation systems. The first part of this thesis is devoted to the pose estimation on the Special Euclidean group \SE(3). A generic globally exponentially stable hybrid estimation scheme for pose (orientation and position) and velocity-bias estimation on \SE(3)\times \mathbb{R}^6 is proposed. Moreover, an explicit hybrid observer, using inertial and landmark position measurements, is provided.
The second part of this thesis is devoted to the problem of simultaneous estimation of the attitude, position and linear velocity for inertial navigation systems (INSs). Three different types of nonlinear observers are developed to handle the following cases: continuous landmark position measurements, intermittent landmark position measurements and continuous stereo bearing measurements. First, a class of nonlinear geometric hybrid observers on the Lie group \SE_2(3), with GES guarantees, using continuous IMU and landmark position measurements is developed. Then, a class of nonlinear state observers, with strong stability guarantees, using intermittent landmark measurements is proposed. Finally, a class of state observers, with strong stability guarantees, directly incorporating body-frame stereo-bearing measurements, is proposed
Nonlinear Observer for Visual-Inertial Navigation Using Intermittent Landmark Measurements
The development of reliable orientation, position and linear velocity estimation algorithms for the 3D visual-inertial navigation system (VINS) is instrumental in many applications, such as autonomous underwater vehicles (AUVs), and unmanned aerial vehicles (UAVs). It is extremely important when the global position system (GPS) is not available in GPS-denied environments. Recently, observers design for VINS using landmark position measurements from Kinect sensors or stereo cameras has been increasingly investigated in the literature.
The aim of this work is to design a nonlinear observer for VINS under the assumption that landmark position measurements are intermittent. In practice, the landmark measurements are not continuous due to computation cost from image processing, which is different from most of the existing results relying on continuous landmark measurements. The proposed nonlinear observer, motivated from the classical linear Kalman filter, has two parts: continuous prediction using inertial measurement unit and previous landmark measurements, and instantaneous state updating upon the arrival of new landmark measurements.
Almost global asymptotic stability (AGAS) has been achieved by applying the framework of the hybrid dynamical system, which means that the estimated state will asymptotically converge to the real state of the visual-inertial navigation system for almost all initial conditions. We strongly believe that our developed estimation tool will not only benefit the area of aerial vehicles engineering but also the robotics and biomedical engineering community
A Global Asymptotic Convergent Observer for SLAM
This paper examines the global convergence problem of SLAM algorithms, an
issue that faces topological obstructions. This is because the state-space of
attitude dynamics is defined on a non-contractible manifold: the special
orthogonal group of order three SO(3). Therefore, this paper presents a novel,
gradient-based hybrid observer to overcome these topological obstacles. The
Lyapunov stability theorem is used to prove the globally asymptotic convergence
of the proposed algorithm. Finally, comparative analyses of two simulations
were conducted to evaluate the performance of the proposed scheme and to
demonstrate the superiority of the proposed hybrid observer to a smooth
observer.Comment: 7 pages, 8 figures, conferenc
Hybrid Controller for Robot Manipulators in Task-Space with Visual-Inertial Feedback
This paper presents a visual-inertial-based control strategy to address the
task space control problem of robot manipulators. To this end, an
observer-based hybrid controller is employed to control end-effector motion. In
addition, a hybrid observer is introduced for a visual-inertial navigation
system to close the control loop directly at the Cartesian space by estimating
the end-effector pose. Accordingly, the robot tip is equipped with an inertial
measurement unit (IMU) and a stereo camera to provide task-space feedback
information for the proposed observer. It is demonstrated through the Lyapunov
stability theorem that the resulting closed-loop system under the proposed
observer-based controller is globally asymptotically stable. Besides this
notable merit (global asymptotic stability), the proposed control method
eliminates the need to compute inverse kinematics and increases trajectory
tracking accuracy in task-space. The effectiveness and accuracy of the proposed
control scheme are evaluated through computer simulations, where the proposed
control structure is applied to a 6 degrees-of-freedom long-reach hydraulic
robot manipulator
Nonlinear Attitude Estimation Using Intermittent and Multi-Rate Vector Measurements
This paper considers the problem of nonlinear attitude estimation for a rigid
body system using intermittent and multi-rate inertial vector measurements as
well as continuous (high-rate) angular velocity measurements. Two types of
hybrid attitude observers on Lie group are proposed. First, we propose
a hybrid attitude observer where almost global asymptotic stability is
guaranteed using the notion of almost global input-to-state stability on
manifolds. Thereafter, this hybrid attitude observer is extended by introducing
a switching mechanism to achieve global asymptotic stability. Both simulation
and experimental results are presented to illustrate the performance of the
proposed hybrid observers.Comment: 22 pages, 7 figures, submitted to IEEE TAC for possible publicatio
InGVIO: A Consistent Invariant Filter for Fast and High-Accuracy GNSS-Visual-Inertial Odometry
Combining Global Navigation Satellite System (GNSS) with visual and inertial
sensors can give smooth pose estimation without drifting in geographical
coordinates. The fusion system gradually degrades to Visual-Inertial Odometry
(VIO) with the number of satellites decreasing, which guarantees robust global
navigation in GNSS unfriendly environments. In this letter, we propose an
open-sourced invariant filter-based platform, InGVIO, to tightly fuse
monocular/stereo visual-inertial measurements, along with raw data from GNSS,
i.e. pseudo ranges and Doppler shifts. InGVIO gives highly competitive results
in terms of accuracy and computational load compared to current graph-based and
`naive' EKF-based algorithms. Thanks to our proposed key-frame marginalization
strategies, the baseline for triangulation is large although only a few cloned
poses are kept. Besides, landmarks are anchored to a single cloned pose to fit
the nonlinear log-error form of the invariant filter while achieving decoupled
propagation with IMU states. Moreover, we exploit the infinitesimal symmetries
of the system, which gives equivalent results for the pattern of degenerate
motions and the structure of unobservable subspaces compared to our previous
work using observability analysis. We show that the properly-chosen invariant
error captures such symmetries and has intrinsic consistency properties. InGVIO
is tested on both open datasets and our proposed fixed-wing datasets with
variable levels of difficulty. The latter, to the best of our knowledge, are
the first datasets open-sourced to the community on a fixed-wing aircraft with
raw GNSS.Comment: 8 pages, 8 figures; manuscript will be submitted to IEEE RA-L for
possible publicatio
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