Image-Based Localization Using Context
Charbel Azzi, John Zelek, Daniel Asmar, Adel H. Fakih · Vision Letters · 2015
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.