Large Scale Acquisition of Complex Environments by Data Fusion from Mobile Visual and Depth Sensors

This thesis addresses the pressing need in industry and Architecture, Engineering, and Construction (AEC) for efficient scanning and modeling of large, structured environments. The primary objective is the development and application of a state-of-the-art wearable system integrating laser and visual scanning technology. This innovative mobile mapping system (MMS) is designed to capture complex man-made structures in diverse settings, ranging from indoor spaces to challenging outdoor environments and underground settings. The MMS combines high-resolution photographic data with laser scanning and inertial inputs to generate detailed, dense point clouds, offering extensive coverage and depth. This approach addresses the limitations of geometric models by providing photorealistic representations essential for applications requiring accurate location recognition, object identification, and content creation. Key contributions of this research include the development of the MMS prototype, capable of adapting to various data acquisition scenarios while ensuring scalability and minimal data redundancy. Additionally, the thesis explores the practical application of the MMS in a pilot AEC project, demonstrating its effectiveness in real-world scenarios for construction monitoring and integration with Building Information Modeling (BIM). Through a combination of technical development, rigorous testing, and practical application, this thesis advances the field of mobile mapping. It opens new avenues for spatial data acquisition and modeling, particularly in environments where traditional mapping techniques fall short.

Grantor dc:publisher

Università degli Studi di Cagliari

Year dc:date

2024

Author dc:creator

• SÁNCHEZ ALCÁZAR, ARMANDO ARTURO

Contributors dc:contributor

• SCATENI, RICCARDO

• Settore INF/01 - Informatica

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

ita

Handle dc:identifier

https://hdl.handle.net/11584/391988

OAI identifier oai:identifier

oai:iris.unica.it:11584/391988