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 keypoints 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.