Depth-Based Safe Landing for Unmanned Aerial Vehicles in GPS-Denied Environment

Abstract

Highlights: What are the main findings? A lightweight computer vision algorithm using a depth camera and standard deviation metric can robustly identify flat zones for UAV landing in GPS-denied environments. The system performs reliable autonomous landings with minimal computational resources, fully integrated via a direct MAVLink connection. What are the implications of the main findings? This work enables safe UAV landing without relying on GPS, external infrastructure, or heavy processing, making it ideal for low-cost drones. The approach supports real-time onboard deployment on constrained hardware, enhancing operational autonomy in unknown environments. This paper presents a safe landing methodology for Unmanned Aerial Vehicles (UAVs) when the GPS-based navigation system fails or is denied or unavailable. The approach relies on the estimation of a flat landing area when landing is required in an unknown area. The proposed system is based on a lightweight computer vision algorithm that enables real-time identification of suitable landing zones using a depth camera and an onboard companion computer. Analysis of small, spatially distributed areas to calculate the mean altitude and standard deviation across regions enables reliable selection of flat surfaces. A robust landing control algorithm is activated when the area meets strict flatness conditions for a continuous period. Real-time experiments confirmed the effectiveness of this approach under disturbances, showing reliable detection of the safe zone and the robustness of the proposed control algorithm in outdoor environments.