From sparse SLAM to dense mapping for UAV autonomous navigation
Résumé
Autonomous or semi-autonomous navigation of UAVs is of great interest in the Defense and Security domains, as it significantly improves their efficiency and responsiveness during operations. The perception of the environment and in particular the dense and metric 3D mapping in real time is a priority for navigation and obstacle avoidance. We therefore present our strategy to jointly estimate a dense 3D map by combining a sparse map estimated by a state-of-the-art Simultaneous Localization and Mapping (SLAM) system and a dense depth map predicted by a monocular self-supervised method. Then, a lightweight and volumetric multi-view fusion solution is used to build and update a voxel map.
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