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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.