LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories

A new procedure supporting filtering and classification of LiDAR data based on both elevation and intensity analysis is introduced and validated. After a preliminary analysis to avoid the trivial classification of homogeneous datasets, a non-parametric estimation of the probability density function is computed for both elevation and intensity data values. Some statistical tests are used for selecting the category of data (elevation or intensity) that better satisfies a bi- or a multi-modal distribution. The iterative analysis of skewness and kurtosis is then applied to this category to obtain a first classification. At each step, the point with the highest value of elevation (or intensity) is removed. The classification is then refined by studying both statistical moments of the complementary data category, in order to look for potential sub-clusters. Remaining clusters are identified by applying the same iterative procedure to the still unclassified LiDAR points. For more complex point distribution shapes or for the classification of large scenes, a progressive analysis is proposed, which is based on the partitioning of the entire dataset into more sub-sets. Each of them is then independently classified by using the core procedure. Some numerical experiments on real LiDAR data confirmed the potentiality of the filtering/classification method

On the Role of Geomatics and Official Regional Cartography in the Interconnected Nord-Est Innovation Ecosystem

The paper presents the expected role of geomatics within the Interconnected Nord-Est Innovation Ecosystem (iNEST), a research and innovation project funded by the Italian National Recovery and Resilience Plan and involving universities, companies and territorial institutions of the Triveneto macro-region. The iNEST ecosystem aims to extend the beneficial effects of digitalization to the key specialization areas of Nord-Est, including technologies for the marine and mountain environment, smart agri-food, architecture, tourism and cultural heritage. In fields such as these, where knowledge of the territory is paramount, up-to-date regional cartographic products are essential. Therefore, this work also gives us the opportunity to provide a comprehensive overview of the cartographic products available for Friuli Venezia Giulia, one of the regions included in the iNEST project

FEASIBILITY AND ACCURACY OF AS-BUILT MODELLING FROM SLAM-BASED POINT CLOUDS: PRELIMINARY RESULTS

Nowadays, portable Mobile Mapping Systems (MMSs) and robotic mapping platforms leveraging on Simultaneous Localization and Mapping (SLAM) methods are gaining increasing attention for architectural and construction surveying, representing an efficient solution for geometric data acquisition for scan-to-BIM purposes. However, the applicability of standard modelling workflows and the accuracy of Building Information Models (BIM) that can be obtained from SLAM-based point clouds is still an open question. In this paper, we propose a preliminary evaluation on the feasibility of extracting as-built BIM from (i) a point cloud acquired with a commercial portable MMS, and (ii) a point cloud obtained through an open-source SLAM algorithm, surveying the environment with an autonomous mobile robotic platform. In both cases, the main structural elements of the test site are accurately generated, thus showing promising results. On the other hand, the experiment highlights also the need for SLAM systems capable of providing less noisy point clouds, in order to capture and model architectural details

Characterization and BMP Tests of Liquid Substrates for High-rate Anaerobic Digestion

This work was focused on the physicochemical characterization and biochemical methane potential (BMP) tests of some liquid organic substrates, to verify if they were suitable for undergoing a process of high-velocity anaerobic digestion. The selected substrates were: first and second cheese whey, organic fraction of municipal solid waste (OFMSW) leachate, condensate water and slaughterhouse liquid waste. Firstly, a physicochemical characterization was performed, using traditional and macromolecular parameters; then, batch anaerobic tests were carried out, and some continuous tests were planned. The results revealed that all the analyzed substrates have a potential to be anaerobically treated. Valuable information about treatment rate for a high-velocity anaerobic digestion process was obtained. Start-up of a lab-scale UASB reactor, treating diluted cheese whey, was successfully achieved with good COD removal efficiency. These preliminary results are expected to be further investigated in a successive phase, where continuous tests will be conducted on condensate water and OFMSW leachate. This work is licensed under a Creative Commons Attribution 4.0 International License

Performance Investigation and Repeatability Assessment of a Mobile Robotic System for 3D Mapping

In this paper, we present a quantitative performance investigation and repeatability assessment of a mobile robotic system for 3D mapping. With the aim of a more efficient and automatic data acquisition process with respect to well-established manual topographic operations, a 3D laser scanner coupled with an inertial measurement unit is installed on a mobile platform and used to perform a high-resolution mapping of the surrounding environment. Point clouds obtained with the use of a mobile robot are compared with those acquired with the device carried manually as well as with a terrestrial laser scanner survey that serves as a ground truth. Experimental results show that both mapping modes provide similar accuracy and repeatability, whereas the robotic system compares favorably with respect to the handheld modality in terms of noise level and point distribution. The outcomes demonstrate the feasibility of the mobile robotic platform as a promising technology for automatic and accurate 3D mapping

Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy)

The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure’s behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure’s behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and GMMs (Gaussian Mixture Models), are used in order to reliably estimate the system poles, and hence the corresponding vibration characteristics. The performance of the considered methods is compared on the Valgadena bridge case study, showing that the use of GMM clustering reduces, with respect to DBSCAN, the impact of the choice of certain parameter values in the considered case