  Classical Visual Simultaneous Localization and Mapping (VSLAM) algorithms can
be easily induced to fail when either the robot's motion or the environment is
too challenging. The use of Deep Neural Networks to enhance VSLAM algorithms
has recently achieved promising results, which we call hybrid methods. In this
paper, we compare the performance of hybrid monocular VSLAM methods with
different learned feature descriptors. To this end, we propose a set of
experiments to evaluate the robustness of the algorithms under different
environments, camera motion, and camera sensor noise. Experiments conducted on
KITTI and Euroc MAV datasets confirm that learned feature descriptors can
create more robust VSLAM systems.
