Lidia Forlenza

Publications (2)1.74 Total impact

• Source

  Available from: PubMed Central

  Article: Real time corner detection for miniaturized electro-optical sensors onboard small unmanned aerial systems.

  Lidia Forlenza, Patrick Carton, Domenico Accardo, Giancarmine Fasano, Antonio Moccia
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  ABSTRACT: This paper describes the target detection algorithm for the image processor of a vision-based system that is installed onboard an unmanned helicopter. It has been developed in the framework of a project of the French national aerospace research center Office National d'Etudes et de Recherches Aérospatiales (ONERA) which aims at developing an air-to-ground target tracking mission in an unknown urban environment. In particular, the image processor must detect targets and estimate ground motion in proximity of the detected target position. Concerning the target detection function, the analysis has dealt with realizing a corner detection algorithm and selecting the best choices in terms of edge detection methods, filtering size and type and the more suitable criterion of detection of the points of interest in order to obtain a very fast algorithm which fulfills the computation load requirements. The compared criteria are the Harris-Stephen and the Shi-Tomasi, ones, which are the most widely used in literature among those based on intensity. Experimental results which illustrate the performance of the developed algorithm and demonstrate that the detection time is fully compliant with the requirements of the real-time system are discussed.
  Sensors 01/2012; 12(1):863-77. · 1.74 Impact Factor
• Conference Proceeding: Automatic Collision Avoidance System: Design, development and flight tests

  Salvatore Luongo, Vittorio Di Vito, Giancarmine Fasano, Domenico Accardo, Lidia Forlenza, Antonio Moccia
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  ABSTRACT: This paper presents a fully Automatic Collision Avoidance System (ACAS) for unmanned aerial vehicles. This system has been developed by the Italian Aerospace Research Center (CIRA) in collaboration with the department of Aerospace Engineering of the University of Naples “Federico II”, in the framework of the national funded research project TECVOL (Technologies for the Autonomous Flight). The proposed system is comprised of two subsystems: the Obstacle Detection and Tracking subsystem, which permits to reveal flying intruders in a selected field of regard and to estimate their motion; the Collision Avoidance subsystem, which provides conflict detection and resolution capabilities, addressed in a 3D environment using information about current position and instantaneous speed vectors. The effectiveness of the system has been demonstrated during a flight test campaign, where proper conflict scenarios have been considered. In fact, the proposed ACAS setup was installed onboard a very light aircraft named FLARE (Flight Laboratory for Aeronautical Research), which has been customized with automatic flight capabilities. System architecture and the developed algorithms are described, then some results obtained from the flight test campaign are presented and discussed which demonstrate the reliability and the efficiency of the developed system.
  Digital Avionics Systems Conference (DASC), 2011 IEEE/AIAA 30th; 11/2011