H-SLAM: Hybrid Direct-Indirect Visual SLAM
Georges Younes Douaa Khalil John Zelek Daniel Asmar
The recent success of hybrid methods in monocular odometry has led to many
attempts to generalize the performance gains to hybrid monocular SLAM. However,
most attempts fall short in several respects, with the most prominent issue
being the need for two different map representations (local and global maps),
with each requiring different, computationally expensive, and often redundant
processes to maintain. Moreover, these maps tend to drift with respect to each
other, resulting in contradicting pose and scene estimates, and leading to
catastrophic failure. In this paper, we propose a novel approach that makes use
of descriptor sharing to generate a single inverse depth scene representation.
This representation can be used locally, queried globally to perform loop
closure, and has the ability to re-activate previously observed map points
after redundant points are marginalized from the local map, eliminating the
need for separate and redundant map maintenance processes. The maps generated
by our method exhibit no drift between each other, and can be computed at a
fraction of the computational cost and memory footprint required by other
monocular SLAM systems. Despite the reduced resource requirements, the proposed
approach maintains its robustness and accuracy, delivering performance
comparable to state-of-the-art SLAM methods (e.g., LDSO, ORB-SLAM3) on the
majority of sequences from well-known datasets like EuRoC, KITTI, and TUM VI.
The source code is available at: https://github.com/AUBVRL/fslam_ros_docker.