Visual Environment Assessment for Safe Autonomous Quadrotor Landing

Autonomous identification and evaluation of safe landing zones are of paramount importance for ensuring the safety and effectiveness of aerial robots in the event of system failures, low battery, or the successful completion of specific tasks. In this thesis it is presented a novel approach, for detecting and assess potential landing sites for safe quadrotor landing. The proposed solution efficiently integrates both 2D and 3D environmental information and eliminates the need for external aids such as GPS and computationally intensive elevation maps. Semantic data derived from a Neural Network (NN), is combined with geometric data obtained from a disparity map, to extract environmental features and critical geometric attributes such as slope, flatness, and roughness. In particular, this method efficiently combines both metric and semantic information, making it also more robust, compared to other solutions that solely relies on one type of information only. Based on those attributes, several cost metrics are defined to evaluate safety, stability, and suitability of regions in the environments and identify the most suitable landing area. In this way we have a comprehensive evaluation of all the relevant aspects related to the safe site detection. This approach runs in real-time on quadrotors equipped with limited computational capabilities. Experimental results conducted in diverse environments demonstrate that the proposed method can effectively assess and identify suitable landing areas, enabling the safe and autonomous landing of a quadrotor in unknown environments.