Nuevas herramientas para la modelización de datos procedentes de sensores/new tools form modelling sensor data

Abstract

The objective of this thesis is to develop a framework capable of handling multiple data sources by correcting and fusing them to monitor, predict, and optimize real-world processes. The scope is not limited to images but also covers the reconstruction of 3D point clouds integrating visible, multispectral, thermal and hyperspectral data. However, working with real-world data is also tedious as it involves multiple steps that must be performed manually, such as collecting data, marking control points or annotating points. Instead, an alternative is to generate synthetic data from realistic scenarios, hence avoiding the acquisition of prohibitive technology and efficiently constructing large datasets. In addition, models in virtual scenarios can be attached to semantic annotations and materials, among other properties. Unlike manual annotations, synthetic datasets do not introduce spurious information that could mislead the algorithms that will use them.