Feature Tracking Evaluation for Pose Estimation in Underwater Environments

In this paper we present the computer vision component of a 6DOF pose estimation algorithm to be used by an underwater robot. Our goal is to evaluate which feature trackers enable us to accurately estimate the 3D positions of features, as quickly as possible. To this end, we perform an evaluation of available detectors, descriptors, and matching schemes, over different underwater datasets. We are interested in identifying combinations in this search space that are suitable for use in structure from motion algorithms, and more generally, vision-aided localization algorithms that use a monocular camera. Our evaluation includes frame-by-frame statistics of desired attributes, as well as measures of robustness expressed as the length of tracked features. We compare the fit of each combination based on the following attributes: number of extracted key points per frame, length of feature tracks, average tracking time per frame, number of false positive matches between frames. Several datasets were used, collected in different underwater locations and under different lighting and visibility conditions.