1,975 research outputs found
Invariant EKF Design for Scan Matching-aided Localization
Localization in indoor environments is a technique which estimates the
robot's pose by fusing data from onboard motion sensors with readings of the
environment, in our case obtained by scan matching point clouds captured by a
low-cost Kinect depth camera. We develop both an Invariant Extended Kalman
Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based
solution to this problem. The two designs are successfully validated in
experiments and demonstrate the advantage of the IEKF design
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
A Nonlinear Observer for Free-Floating Target Motion using only Pose Measurements
In this paper, we design a nonlinear observer to estimate the inertial pose
and the velocity of a free-floating non-cooperative satellite (Target) using
only relative pose measurements. In the context of control design for orbital
robotic capture of such a non-cooperative Target, due to lack of navigational
aids, only a relative pose estimate may be obtained from slow-sampled and noisy
exteroceptive sensors. The velocity, however, cannot be measured directly. To
address this problem, we develop a model-based observer which acts as an
internal model for Target kinematics/dynamics and therefore, may act as a
predictor during periods of no measurement. To this end, firstly, we formalize
the estimation problem on the SE(3) Lie group with different state and
measurement spaces. Secondly, we develop the kinematics and dynamics observer
such that the overall observer error dynamics possesses a stability property.
Finally, the proposed observer is validated through robust Monte-Carlo
simulations and experiments on a robotic facility.Comment: 8 pages, 6 figure
Symmetry-preserving Observers
This paper presents three non-linear observers on three examples of
engineering interest: a chemical reactor, a non-holonomic car, and an inertial
navigation system. For each example, the design is based on physical
symmetries. This motivates the theoretical development of invariant observers,
i.e, symmetry-preserving observers. We consider an observer to consist in a
copy of the system equation and a correction term, and we give a constructive
method (based on the Cartan moving-frame method) to find all the
symmetry-preserving correction terms. They rely on an invariant frame (a
classical notion) and on an invariant output-error, a less standard notion
precisely defined here. For each example, the convergence analysis relies also
on symmetries consideration with a key use of invariant state-errors. For the
non-holonomic car and the inertial navigation system, the invariant
state-errors are shown to obey an autonomous differential equation independent
of the system trajectory. This allows us to prove convergence, with almost
global stability for the non-holonomic car and with semi-global stability for
the inertial navigation system. Simulations including noise and bias show the
practical interest of such invariant asymptotic observers for the inertial
navigation system.Comment: To be published in IEEE Automatic Contro
Vision technology/algorithms for space robotics applications
The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed
Aeronautical Engineering: A continuing bibliography, supplement 120
This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980
Advancements and Breakthroughs in Ultrasound Imaging
Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world
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