Vision based Autonomous Landing in Populated Environments a crucial aspect for urban deployment
Abstract
Safe autonomous landing in populated environments is a new and challenging open problem which will help to unleash the true potential of drones in applications in urban environments. Up to date, the use of drones has been limited to controlled scenarios, far awayfrom people, given the risk of accidents in case of system failure or human error, hence, more robust and resilient strategies, including autonomous emergency landing protocols, are indispensable in modern applications to guarantee the safe use of drones while operating in populated areas. Accordingly, we propose to use vision-based techniques along with modern Deep Learning approaches (Semantic Segmentation, Object Detection, Density Maps, etc.) to detect potential safe landing zones in unknown, unstructured and changingscenarios, like the ones encountered in urban areas. Semantic segmentation is proposed to classify different regions according to the level of risk of accidents, especially involving humans. Also, density maps will be used to locate people on the scene. Finally, a suitable landing zone should be selected trying to minimize the risk of accident, and the drone must land autonomously. For safetyreasons, the proposed strategy will be tested in realistic virtual environments with a real drone (robot-in-the-loop), before deployment in real scenarios.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 24, 2024
- Source ID
- N629092412001
Entities
People
- Diego Mercado Ravell
Organizations
- CIMAT Center for Mathematical Research
- Office of Naval Research
- United States Navy