Image-Based Localization Using Context

Abstract

Image-based localization problem consists of estimating the 6 DoF
camera pose by matching the image to a 3D point cloud (or equivalent)
representing a 3D environment. The robustness and accuracy
of current solutions is not objective and quantifiable. We
have completed a comparative analysis of the main state of the art
approaches, namely Brute Force Matching, Approximate Nearest
Neighbour Matching, Embedded Ferns Classification, ACG Localizer(
Using Visual Vocabulary) and Keyframe Matching Approach.
The results of the study revealed major deficiencies in each approach
mainly in search space reduction, clustering, feature matching
and sensitivity to where the query image was taken. Then, we
choose to focus on one common major problem that is reducing
the search space. We propose to create a new image-based localization
approach based on reducing the search space by using
global descriptors to find candidate keyframes in the database then
search against the 3D points that are only seen from these candidates
using local descriptors stored in a 3D cloud map.