Loianno, Giuseppe (2014) The Role of Vision Algorithms for Micro Aerial Vehicles. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | The Role of Vision Algorithms for Micro Aerial Vehicles |
| Autori: | Autore Email Loianno, Giuseppe giuseppe.loianno@unina.it |
| Data: | 31 Marzo 2014 |
| Numero di pagine: | 132 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Scuola di dottorato: | Ingegneria dell'informazione |
| Dottorato: | Ingegneria informatica ed automatica |
| Ciclo di dottorato: | 26 |
| Coordinatore del Corso di dottorato: | nome email Garofalo, Francesco francesco.garofalo@unina.it |
| Tutor: | nome email Lippiello, Vincenzo [non definito] Kumar, Vijay [non definito] |
| Data: | 31 Marzo 2014 |
| Numero di pagine: | 132 |
| Parole chiave: | Micro Aerial Vehicles, Computer Vision, Sensor Fusion, Estimation, Robotics |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/04 - Automatica |
| Depositato il: | 14 Apr 2014 05:56 |
| Ultima modifica: | 28 Gen 2015 09:45 |
| URI: | http://www.fedoa.unina.it/id/eprint/9858 |
Abstract
This work investigates the research topics related to visual aerial navigation in loosely structured and cluttered environments. During the inspection of the desired infrastructure the robot is required to fly in an environment which is uncertain and only partially structured because, usually, no reliable layouts and drawings of the surroundings are available. To support these features, advanced cognitive capabilities are required, and in particular the role played by vision is of paramount importance. The use of vision and other onboard sensors such as IMU and GPS play a fundamental to provide high level degree of autonomy to flying vehicles. In detail, the outline of this thesis is organized as follows • Chapter 1 is a general introduction of the aerial robotic field, the quadrotor platform, the use of onboard sensors like cameras and IMU for autonomous navigation. A discussion about camera modeling, current state of art on vision based control, navigation, environment reconstruction and sensor fusion is presented. • Chapter 2 presents vision based control algorithms useful for reactive control like collision avoidance, perching and grasping tasks. Two main contributions are presented based on relative depth map and image based visual servoing respectively. • Chapter 3 discusses the use of vision algorithms for localization and mapping. Compared to the previous chapter, the vision algorithm is more complex involving vehicle’s poses estimation and environment reconstruction. An algorithm based on RGB-D sensors for localization, extendable to localization of multiple vehicles, is presented. Moreover, an environment representation for planning purposes, applied to industrial environments, is introduced. • Chapter 4 introduces the possibility to combine vision measurements and IMU to estimate the motion of the vehicle. A new contribution based on Pareto Optimization, which overcome classical Kalman filtering techniques, is presented. • Chapter 5 contains conclusion, remarks and proposals for possible developments.
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