DOCUMENTATION OF COMPLEX ENVIRONMENTS IN CULTURAL HERITAGE SITES. A SLAM-BASED SURVEY IN THE CASTELLO DEL VALENTINO BASEMENT

Underground Built Heritage (UBH) stands out among the existing Cultural Heritage sites as a peculiar scenario. The assets belonging to this type of heritage are typically difficult to manage, exploit, and promote because of a lack of knowledge and documentation. The challenges in documenting built heritage are many and wide-ranging, and the main need must be to provide an accurate and appropriate representation of the surveyed area and its geometric features without employing time-consuming processes. Mobile Mapping Systems (MMSs) are nowadays trending technologies for the geomatics community, proving to be a useful alternative to traditional surveying techniques when taking time and cost constraints into account. The paper focuses on the use of an MMS, the STONEX® X120GO SLAM Laser Scanner system, in documenting a portion of the Castello del Valentino, an articulated and complex architecture located in Turin (Italy). The underground floor of the castle, due to its complexity in terms of accessibility and the challenge it poses for the documentation approach, was chosen as a case study to assess the STONEX® X120GO's capabilities in terms of portability of the instrument, speed of acquisition, as well as completeness and accuracy of the acquired dataset. The results obtained using the MMS technique have been compared to and validated using data from a TLS (Terrestrial Laser Scanner) survey used as a ground reference. The results and considerations reported in this paper demonstrate that MMSs can accurately and completely depict built spaces and their main characteristics and have substantial potential in mapping complex assets

GEOMATIC CONTRIBUTION FOR THE RESTORATION PROJECT OF THE VALENTINO CASTLE GREEN ROOM. FROM DATA ACQUISITION TO INTEGRATED DOCUMENTATION

In the framework of restoration operations, valuable assistance can be supplied from innovative techniques and methods developed in the field of Geomatics. Over the years, this continuous collaboration has produced synergistic and interdisciplinary results that have been successfully contributing to heritage conservation and valorisation. In the case of the current research, thorough multisensory investigations have been performed in order to provide a deeper knowledge of the Green Room of the Valentino Castle in Turin and to support the planning of the future restoration works that will involve this valuable asset. In the framework of this experience, four LiDAR systems have been employed in order to evaluate the different results obtainable from the sensors. Additionally, a complete photogrammetric close-range survey has been carried out, and some tests were completed using a hyperspectral camera. The workflow followed during the current research is described in this paper, and a comparison between the obtained outputs is proposed, focusing on the characteristics of these metric products, useful and sometimes necessary in the framework of the restoration project. Besides, some considerations on the advantages and the issues connected with the use of these reality-based data as a starting point for HBIM (Heritage Building Information Modeling) model generation are proposed, along with some observations about the potentialities of a photogrammetric co-registration approach using spectrum technologies for deterioration/decay detection and monitoring of heritage

THE IPAD PRO BUILT-IN LIDAR SENSOR: 3D RAPID MAPPING TESTS AND QUALITY ASSESSMENT

The main goal of this ongoing research is the evaluation of the iPad Pro built-in LiDAR sensor for large scale 3D rapid mapping. Different aspects have been considered from the architectural surveying perspective and several analyses were carried out focusing on the acquisition phase and the definition of best practices for data collection, the quantitative analysis on the acquired data and their 3D positional accuracy assessment, and the qualitative analysis of the achievable metric products. Despite this paper is a preliminary analysis and deeper studies in various application environment are necessary, the availability of a LiDAR sensor embedded in a tablet or mobile phone, appears promising for rapid surveying purposes. According to test outcomes, the sensor is able to rapidly acquire reliable 3D point clouds suitable for 1:200 architectural rapid mapping; the iPad Pro could represent an interesting novelty also thanks to its price (compared to standard surveying instruments), portability and limited time required both for data acquisition and processing

Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network

Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches

Engineering Reconnaissance following the October 2016 Central Italy Earthquakes. Version 2. Editors Paolo Zimmaro and Jonathan Stewart, Geotechnical Earthquake Engineering Reconnaissance GEER Association, Report No. GEER-050D

A team from the Geotechnical Extreme Events Reconnaissance (GEER) Association, supported by the National Science Foundation, has been mobilized to investigate geotechnical and geological aspects of the destructive earthquake sequence that occurred in Central Italy during a series of significant events October 26-30, 2016, which followed prior events August 24-29, 2016. GEER responded to the initial event sequence and reports resulting from that effort are published on the GEER web site. As before, GEER will operate in close collaboration with Italian engineers and scientists. GEER is also coordinating its reconnaissance activities to coincide with those of EERI, which will be led by Dr. Silvia Mazzoni. Giuseppe Lanzo, Professor at Sapienza University of Rome, and Jonathan P. Stewart, Professor and Chair of the Department of Civil and Environmental Engineering at UCLA, are the GEER team co-leaders. The US-based GEER team members participating in the investigation are Prof. Kevin Franke (Brigham Young University), Dr. Robert E. Kayen (US Geological Survey and UCLA), and Dr. Bret Lingwall (South Dakota School of Mines and Tech.). The GEER team is part of an international coordinated effort that involves cognizant Italian agencies (i.e. National Institute of Geophysics and Vulcanology, INGV; Rete dei Laboratori Universitari di Ingegneria Sismica, ReLuis; and European Centre for Training and Research in Earthquake Engineering, EUCENTRE Foundation and Italian Center for Seismic Microzonation and its Applications). Key Italian participants include: Prof. Luigi Di Sarno (ReLuis and University of Sannio), Profs. Sebastiano Foti and Filiberto Chiabrando (Politecnico di Torino), Dr. Fabrizio Galadini, Emanuela Falcucci, and Stefano Gori (INGV), Prof. Alessandro Pagliaroli (University of Chieti-Pescara), Dr. Giuseppe Scasserra and Prof. Filippo Santucci de Magistris (University of Molise), Prof. Francesco Silvestri (University of Napoli Federico II), Prof. Stefano Aversa (University of Napoli Parthenope) and MrDr. Paolo Tommasi (Consiglio Nazionale delle Ricerche, Rome). Also contributing to the GEER effort are researchers from New Zealand (Dr. Fernando Della Pasqua, GNS Science) and United Kingdom/Greece (team led by Prof. Anastasios Sextos, University of Bristol and Aristotle University of Thessaloniki). A full list of GEER team members will be compiled following deployment to the field. The GEER team assembled for this effort is multi-disciplinary, including geology, seismology, geotechnical engineering, structural engineering, and geomatics. Based on information gathered to date, field investigations for the GEER team and collaborators have focused on: (1) substantial surface fault rupture, apparently on the Mt. Vettore fault, (2) major rockfalls and landslides, including a large slide that dammed a river; and (3) building, bridge, and other infrastructure performance in villages and hamlets throughout the region, including many that had been well documented in reconnaissance following the 24-29 August event sequence. Earthquake engineering is an experience-driven field in which perishable data that can be used to advance our understanding should be systematically collected. The data collection will be performed using traditional mapping/observational methods and advanced imaging tools