With the advancement of new technologies, it is now possible to create immersive environments for the remote assessment of structural integrity. By leveraging images captured periodically by robots or drones, the inspection process can be automated and managed remotely, even in challenging and hard-to-reach environments. This approach significantly enhances the efficiency and accuracy of structural monitoring, reducing the need for on-site manual inspections and improving overall safety.

This paper presents the initial findings of a research project aimed at developing a comprehensive framework for the automatic detection of structural defects.

The proposed approach consists of two main steps. The former comprises the acquisition of a high-resolution point cloud map of the structure using a LiDAR sensor. This map is then imported into a simulation engine (e.g., Unity), enabling immersive visualization through Virtual Reality (VR). The use of a VR headset enables the operator to freely explore the virtual environment, replicating the experience of an on-site inspection in a safe and controlled setting.

The latter step includes the utilization of drones to conduct regular inspections, capturing high-resolution images that initially serve as a basis for autonomous exploration and navigation in unstructured environments, and subsequently enable the analysis of cracks, damage progression, and other structural anomalies over time. Due to payload and space constraints of Micro Aerial Vehicles (MAVs),  this approach solely relies on a single stereo camera mounted onboard, that is used for (i) relocalization within a pre-acquired point cloud map, and (ii) capturing images for further analysis.

Finally, the captured images are integrated into the immersive environment, allowing the operator to request visual data from specific areas of interest, particularly where structural damage has been identified, and to monitor the evolution of such anomalies over time.

The methodology has been applied to a masonry church currently exhibiting significant crack patterns, demonstrating the potential for early warning and proactive maintenance in heritage preservation and modern infrastructure management.