Semi-global techniques in image matching and change detection with applications to civil and environmental engineering.

This work of thesis wants to present a dissertation of the wide range of modern dense matching algorithms, which are spreading in different application and research fields, with a particular attention to the innovative “Semi-Global” matching techniques. The choice of develop a semi-global numerical code was justified by the need of getting insight on the variables and strategies that affect the algorithm performances with the primary objective of maximizing the method accuracy and efficiency, and the results level of completeness. The dissertation will consist in the metrological characterization of the proprietary implementation of the semi-global matching algorithm, evaluating the influence of several matching variables and functions implemented in the process and comparing the accuracy and completeness of different results (digital surface models, disparity maps and 2D displacement fields) obtained using our code and other commercial and open-source matching programs in a wide variety of application fields.

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.

Tuning the functional properties of Silicon Carbide Thin Film and Nanowires

The present thesis has been devoted to the synthesis and investigation of functional properties of silicon carbide thin films and nanowires. The work took profit from the experience of the research group in the synthesis of 3C-SiC from vapour phase. 3C-SiC thin films Thin films heteroepitaxy on silicon substrates was carried out in a vapour phase epitaxy reactor. The initial efforts were committed to the process development in order to enhance the crystal quality of the epi-layer. The carbonization process and a buffer layer procedure were optimized in order to obtain good quality monocrystalline 3C-SiC layers. The films characterization was used not only to improve the entire process, but also to assess the crystalline quality and to identify the defects. Methyltrichlorosilane (MTS) was introduced during the synthesis to increase the growth rate and enhance crystalline quality. The effect of synthesis parameters such as MTS flow and process temperature was studied in order to promote defect density reduction and the release of the strain due to lattice mismatch between 3C-SiC and silicon substrate. In-growth n-type doping was implemented using a nitrogen gas line and the effect of different synthesis parameters on doping level was studied. Raman measurements allowed a contactless characterization and evaluation of electrically active dopant. The effect of MTS on nitrogen incorporation was investigated and a promotion of dopant concentration together with a higher growth rate were demonstrated. This result allows to obtain higher doping concentrations without deteriorating crystal quality in 3C-SiC and, to the best of our knowledge, it has never been demonstrated before. 3C-SiC nanowires Core-shell SiC-SiO2 nanowires were synthesized using a chemical vapour deposition technique in an open tube configuration reactor on silicon substrates. Metal catalyst were used to promote a uniaxial growth and a dense bundle of nanowires 100 µm long and 60 nm thick was obtained. Substrate preparation was found to be fundamental in order to obtain a uniform nanowire density. Morphological characterization was carried out using scanning electron microscopy and the analysis of structural, compositional, optical properties is reported.