3,003 research outputs found
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
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
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
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