32,925 research outputs found
Small Area Shrinkage Estimation
The need for small area estimates is increasingly felt in both the public and
private sectors in order to formulate their strategic plans. It is now widely
recognized that direct small area survey estimates are highly unreliable owing
to large standard errors and coefficients of variation. The reason behind this
is that a survey is usually designed to achieve a specified level of accuracy
at a higher level of geography than that of small areas. Lack of additional
resources makes it almost imperative to use the same data to produce small area
estimates. For example, if a survey is designed to estimate per capita income
for a state, the same survey data need to be used to produce similar estimates
for counties, subcounties and census divisions within that state. Thus, by
necessity, small area estimation needs explicit, or at least implicit, use of
models to link these areas. Improved small area estimates are found by
"borrowing strength" from similar neighboring areas.Comment: Published in at http://dx.doi.org/10.1214/11-STS374 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Efficient 2D-3D Matching for Multi-Camera Visual Localization
Visual localization, i.e., determining the position and orientation of a
vehicle with respect to a map, is a key problem in autonomous driving. We
present a multicamera visual inertial localization algorithm for large scale
environments. To efficiently and effectively match features against a pre-built
global 3D map, we propose a prioritized feature matching scheme for
multi-camera systems. In contrast to existing works, designed for monocular
cameras, we (1) tailor the prioritization function to the multi-camera setup
and (2) run feature matching and pose estimation in parallel. This
significantly accelerates the matching and pose estimation stages and allows us
to dynamically adapt the matching efforts based on the surrounding environment.
In addition, we show how pose priors can be integrated into the localization
system to increase efficiency and robustness. Finally, we extend our algorithm
by fusing the absolute pose estimates with motion estimates from a multi-camera
visual inertial odometry pipeline (VIO). This results in a system that provides
reliable and drift-less pose estimation. Extensive experiments show that our
localization runs fast and robust under varying conditions, and that our
extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure
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