  The Simultaneous Localization and Mapping (SLAM) problem addresses the
possibility of a robot to localize itself in an unknown environment and
simultaneously build a consistent map of this environment. Recently, cameras
have been successfully used to get the environment's features to perform SLAM,
which is referred to as visual SLAM (VSLAM). However, classical VSLAM
algorithms can be easily induced to fail when either the motion of the robot or
the environment is too challenging. Although new approaches based on Deep
Neural Networks (DNNs) have achieved promising results in VSLAM, they still are
unable to outperform traditional methods. To leverage the robustness of deep
learning to enhance traditional VSLAM systems, we propose to combine the
potential of deep learning-based feature descriptors with the traditional
geometry-based VSLAM, building a new VSLAM system called LIFT-SLAM. Experiments
conducted on KITTI and Euroc datasets show that deep learning can be used to
improve the performance of traditional VSLAM systems, as the proposed approach
was able to achieve results comparable to the state-of-the-art while being
robust to sensorial noise. We enhance the proposed VSLAM pipeline by avoiding
parameter tuning for specific datasets with an adaptive approach while
evaluating how transfer learning can affect the quality of the features
extracted.
