Towards Global Localization Using Global Descriptors

Abstract

3D pose of a camera with respect to a 3D representation of the
scene. IBL, despite being a trivial problem for small scenes, becomes
quite challenging as the size of the scene grows. Aside from
the computational burden, matching against a very large number
of 3D keypoints spanning a wide variety of viewpoints, illumination,
and areas is a very unreliable process that results in a large
number of outliers and ambiguous situations. In recent years, a
number of approaches have attempted to address the problem using
paradigms such as bag-of-words, features co-occurrence and
others, with varying degrees of success. This paper explores the
use of global descriptors, in particular GIST, to tackle this problem.
We present a system that relies on a similarity measure derived
from GIST to qualify a limited number of 3D points for the matching
process, hence reducing the problem to its small size counterpart.
Our results on a standard dataset show that our system can
achieve better localization accuracy than the state of the art at a
fraction of the computational cost, which can used towards global
localization.