(Georgia Institute of Technology, 2010)
Alcantarilla, Pablo F.; Oh, Sang Min; Mariottini, Gian Luca; Bergasa, Luis M.; Dellaert, Frank
We aim to perform robust and fast
vision-based localization using a pre-existing large
map of the scene. A key step in localization is associating the features extracted from the image with
the map elements at the current location. Although
the problem of data association has greatly benefited
from recent advances in appearance-based matching
methods, less attention has been paid to the effective
use of the geometric relations between the 3D map
and the camera in the matching process.
In this paper we propose to exploit the geometric
relationship between the 3D map and the camera
pose to determine the visibility of the features. In
our approach, we model the visibility of every map
feature w.r.t. the camera pose using a non-parametric
distribution model. We learn these non-parametric
distributions during the 3D reconstruction process,
and develop efficient algorithms to predict the visibility of features during localization. With this approach,
the matching process only uses those map features
with the highest visibility score, yielding a much
faster algorithm and superior localization results. We
demonstrate an integrated system based on the proposed idea and highlight its potential benefits for the
localization in large and cluttered environments.