Abstract
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.
Highlights
Considering the robustness problems that a Unmanned Aerial Vehicle (UAV) faces during an autonomous mission, this paper addresses developing an algorithm for detecting, storing, and selecting real-time emergency landing spots based on Light Detection and Ranging (LiDAR) data
Modular Open Robots Simulation Engine (MORSE) is an open-source simulator with several features, such as Robotic Operating System (ROS) support and virtual sensors, including a generic 3D LiDAR that performs a 180-degree scan and publishes as a point cloud message
This paper addressed the development of a real-time emergency landing detection method based on LiDAR for UAVs
Summary
There have been several research topics related to hardware development, human-system interaction, obstacle detection, and collision avoidance [1]. The UAVs are designed to be remotely controlled by a human operator or execute a mission autonomously [2]. In the latter case, the degree of autonomy and the mission they can achieve depends on the sensors used. Considering the state of the art of UAVs, commercial or research, there is a large spectrum of applications, such as search-and-rescue operations [4,5], delivery, surveillance, inspection, and interaction with the environment [6,7]
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