76,680 research outputs found
Model-based estimation of off-highway road geometry using single-axis LADAR and inertial sensing
This paper applies some previously studied extended
Kalman filter techniques for planar road geometry estimation
to the domain of autonomous navigation of off-highway
vehicles. In this work, a clothoid model of the road geometry is
constructed and estimated recursively based on road features
extracted from single-axis LADAR range measurements. We
present a method for feature extraction of the road centerline
in the image plane, and describe its application to recursive
estimation of the road geometry. We analyze the performance of
our method against simulated motion of varied road geometries
and against closed-loop detection, tracking and following of
desert roads. Our method accomodates full 6 DOF motion of
the vehicle as it navigates, constructs consistent estimates of the
road geometry with respect to a fixed global reference frame,
and requires an estimate of the sensor pose for each range
measurement
Compact continuum source-finding for next generation radio surveys
We present a detailed analysis of four of the most widely used radio source
finding packages in radio astronomy, and a program being developed for the
Australian Square Kilometer Array Pathfinder (ASKAP) telescope. The four
packages; SExtractor, SFind, IMSAD and Selavy are shown to produce source
catalogues with high completeness and reliability. In this paper we analyse the
small fraction (~1%) of cases in which these packages do not perform well. This
small fraction of sources will be of concern for the next generation of radio
surveys which will produce many thousands of sources on a daily basis, in
particular for blind radio transients surveys. From our analysis we identify
the ways in which the underlying source finding algorithms fail. We demonstrate
a new source finding algorithm Aegean, based on the application of a Laplacian
kernel, which can avoid these problems and can produce complete and reliable
source catalogues for the next generation of radio surveys.Comment: 14 pages, 12 figures, accepted for publication in MNRA
Discrete spherical means of directional derivatives and Veronese maps
We describe and study geometric properties of discrete circular and spherical
means of directional derivatives of functions, as well as discrete
approximations of higher order differential operators. For an arbitrary
dimension we present a general construction for obtaining discrete spherical
means of directional derivatives. The construction is based on using the
Minkowski's existence theorem and Veronese maps. Approximating the directional
derivatives by appropriate finite differences allows one to obtain finite
difference operators with good rotation invariance properties. In particular,
we use discrete circular and spherical means to derive discrete approximations
of various linear and nonlinear first- and second-order differential operators,
including discrete Laplacians. A practical potential of our approach is
demonstrated by considering applications to nonlinear filtering of digital
images and surface curvature estimation
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