Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments

Abstract

Safe autonomous landing for Unmanned Aerial Vehicles (UAVs) in populated areas is a crucial aspect for successful urban deployment, particularly in emergency landing situations. Nonetheless, validating autonomous landing in real scenarios is a challenging task involving a high risk of injuring people. In this work, we propose a framework for real-time safe and thorough evaluation of vision-based autonomous landing in populated scenarios, using photo-realistic virtual environments. We propose to use the Unreal graphics engine coupled with the AirSim plugin for drone s simulation, and evaluate autonomous landing strategies based on visual detection of Safe Landing Zones (SLZ) in populated scenarios. Then, we study two different criteria for selecting the "best" SLZ, and evaluate them during autonomous landing of a virtual drone in different scenarios and conditions, under different distributions of people in urban scenes, including moving people. We evaluate different metrics to quantify the performance of the landing strategies, establishing a baseline for comparison with future works in this challenging task, and analyze them through an important number of randomized iterations. The study suggests that the use of the autonomous landing algorithms considerably helps to prevent accidents involving humans, which may allow to unleash the full potential of drones in urban environments near to people.

Vision-based safe autonomous UAV docking with panoramic sensors

“…Furthermore, a series of indoors experiments has proven the system's reliability in enabling landing UAVs to avoid surrounding pedestrians. Rather than completely replacing available onboard methods (Marcu et al, 2018;Tovanche-Picon et al, 2022), our solution serves as an extra layer of safety for UAV landing applications. Our ultimate goal is a collaborative autonomy approach where sensor and detection data from the microairports is fused with the UAVs' sensors and computational capabilities to enhance the system's reliability, safety, and efficiency.…”

“…Several other works have also presented onboard methods that select safe landing zones by detecting potential hazards on the landing path (Alam and Oluoch, 2021). In these papers, the authors utilize lightweight convolutional neural networks such as YOLO (Safadinho et al, 2020) and MobileNet (Castellano et al, 2020) to detect safe landing zones, which are away from individual or groups of people in populated areas (Tovanche-Picon et al, 2022), or flat and obstacle-free areas (Marcu et al, 2018).…”

Evaluation of Safe Landing Site Detection Methods for Unmanned Aerial Vehicles

“…However, the methods were based on Pose correction (Compute the marker Position) and a DL algorithm (For detection and Classification) to choose the safest landing zone. When the system detects the marker move toward the pose correction and computes the appropriate movement of the drone [64] 17 Decision Tree SVM (Support Vector Machine) and DT (Decision Tree) methods were proposed for segmentation-based vision in selecting safe landing sites, which are powerful and widely used classification and prediction tools. Multiple image fusion datasets were utilized to generate distinct feature vectors, and a Digital Image Processing Technique achieved a performance of over 80%.…”