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