A Vision-Based Framework for Safe Landing Zone Mapping of UAVs in Dynamic Environments

Identifying safe landing zones (SLZ) for Unmanned Aerial Vehicles (UAVs) is important to ensure safe navigation, especially when operated in critical environments. However, this is a challenging task, especially in dynamic scenes characterized by concurrent movement of obstacles and UAVs. This paper proposes a vision-based framework that maps SLZs in dynamic scenes, characterized by both object and drone motions, by integrating several functionalities for visual analysis of the UAV surrounding scenes. Static scene analysis is achieved through context-aware segmentation which divides the UAV-acquired images into thematic classes enabling to identify suitable landing surfaces (e.g., roads, grass).For dynamic content analysis, we combine object detection, tracking, and trajectory prediction to determine object occupancy and identify regions free of obstacles. Trajectory prediction is performed through a novel encoder-decoder architecture taking past object positions to predict the most likely future locations. To ensure stable and robust trajectory prediction, we introduce an optimized homography computation using multi-scale image analysis and cumulative updates to compensate UAV motion. We tested our framework on different operational scenarios, including urban and natural scenes with moving objects like vehicles and pedestrians. Obtained results demonstrate its strong performance, and its significant potential for enabling autonomous and safe UAV navigation.