42,883 research outputs found
Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation
This paper derives a contact-aided inertial navigation observer for a 3D
bipedal robot using the theory of invariant observer design. Aided inertial
navigation is fundamentally a nonlinear observer design problem; thus, current
solutions are based on approximations of the system dynamics, such as an
Extended Kalman Filter (EKF), which uses a system's Jacobian linearization
along the current best estimate of its trajectory. On the basis of the theory
of invariant observer design by Barrau and Bonnabel, and in particular, the
Invariant EKF (InEKF), we show that the error dynamics of the point
contact-inertial system follows a log-linear autonomous differential equation;
hence, the observable state variables can be rendered convergent with a domain
of attraction that is independent of the system's trajectory. Due to the
log-linear form of the error dynamics, it is not necessary to perform a
nonlinear observability analysis to show that when using an Inertial
Measurement Unit (IMU) and contact sensors, the absolute position of the robot
and a rotation about the gravity vector (yaw) are unobservable. We further
augment the state of the developed InEKF with IMU biases, as the online
estimation of these parameters has a crucial impact on system performance. We
evaluate the convergence of the proposed system with the commonly used
quaternion-based EKF observer using a Monte-Carlo simulation. In addition, our
experimental evaluation using a Cassie-series bipedal robot shows that the
contact-aided InEKF provides better performance in comparison with the
quaternion-based EKF as a result of exploiting symmetries present in the system
dynamics.Comment: Published in the proceedings of Robotics: Science and Systems 201
Accurate 3D maps from depth images and motion sensors via nonlinear Kalman filtering
This paper investigates the use of depth images as localisation sensors for
3D map building. The localisation information is derived from the 3D data
thanks to the ICP (Iterative Closest Point) algorithm. The covariance of the
ICP, and thus of the localization error, is analysed, and described by a Fisher
Information Matrix. It is advocated this error can be much reduced if the data
is fused with measurements from other motion sensors, or even with prior
knowledge on the motion. The data fusion is performed by a recently introduced
specific extended Kalman filter, the so-called Invariant EKF, and is directly
based on the estimated covariance of the ICP. The resulting filter is very
natural, and is proved to possess strong properties. Experiments with a Kinect
sensor and a three-axis gyroscope prove clear improvement in the accuracy of
the localization, and thus in the accuracy of the built 3D map.Comment: Submitted to IROS 2012. 8 page
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
Astrometric calibration and performance of the Dark Energy Camera
We characterize the ability of the Dark Energy Camera (DECam) to perform
relative astrometry across its 500~Mpix, 3 deg^2 science field of view, and
across 4 years of operation. This is done using internal comparisons of ~4x10^7
measurements of high-S/N stellar images obtained in repeat visits to fields of
moderate stellar density, with the telescope dithered to move the sources
around the array. An empirical astrometric model includes terms for: optical
distortions; stray electric fields in the CCD detectors; chromatic terms in the
instrumental and atmospheric optics; shifts in CCD relative positions of up to
~10 um when the DECam temperature cycles; and low-order distortions to each
exposure from changes in atmospheric refraction and telescope alignment. Errors
in this astrometric model are dominated by stochastic variations with typical
amplitudes of 10-30 mas (in a 30 s exposure) and 5-10 arcmin coherence length,
plausibly attributed to Kolmogorov-spectrum atmospheric turbulence. The size of
these atmospheric distortions is not closely related to the seeing. Given an
astrometric reference catalog at density ~0.7 arcmin^{-2}, e.g. from Gaia, the
typical atmospheric distortions can be interpolated to 7 mas RMS accuracy (for
30 s exposures) with 1 arcmin coherence length for residual errors. Remaining
detectable error contributors are 2-4 mas RMS from unmodelled stray electric
fields in the devices, and another 2-4 mas RMS from focal plane shifts between
camera thermal cycles. Thus the astrometric solution for a single DECam
exposure is accurate to 3-6 mas (0.02 pixels, or 300 nm) on the focal plane,
plus the stochastic atmospheric distortion.Comment: Submitted to PAS
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