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In this paper, we present a publicly available benchmark dataset on multisensorial indoor mapping and positioning (MiMAP), which is sponsored by ISPRS scientific initiatives. The benchmark dataset includes point clouds captured by an indoor mobile laser scanning system in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) LiDAR-based Simultaneous Localization and Mapping (SLAM); (2) automated Building Information Model (BIM) feature extraction; and (3) multisensory indoor positioning. The MiMAP project provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone-based indoor positioning methods. This paper describes the multisensory setup, data acquisition process, data description, challenges, and evaluation metrics included in the MiMAP project.

This paper presents the design of the benchmark dataset on multisensory indoor mapping and position (MIMAP) which is sponsored by ISPRS scientific initiatives. The benchmark dataset including point clouds captured by indoor mobile laser scanning system (IMLS) in indoor environments of various complexity. The benchmark aims to stimulate and promote research in the following three fields: (1) SLAM-based indoor point cloud generation; (2) automated BIM feature extraction from point clouds, with an emphasis on the elements, such as floors, walls, ceilings, doors, windows, stairs, lamps, switches, air outlets, that are involved in building management and navigation tasks ; and (3) low-cost multisensory indoor positioning, focusing on the smartphone platform solution. MIMAP provides a common framework for the evaluation and comparison of LiDAR-based SLAM, BIM feature extraction, and smartphone indoor positioning methods.

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.

Front-end amplifiers with high input impedance are exploited to build active array elements for modern low-frequency radio telescopes. The combination of such loading and mutual coupling between adjacent elements is responsible for the deep nulls that are present in some embedded element patterns (EEPs) of the Low-Frequency Array. This paper quantifies this phenomenon with both models and experimental data. The full-wave array simulations and the measured amplifier scattering parameters are combined to obtain the EEP models. These models are in good agreement with the in situ measurements that have been performed with a micro unmanned aerial vehicle carrying a field source.

Nowadays, there are several strong motivation to replace some dangerous human activities with the use of small unmanned system, both terrestrial and aerial. An interesting example is the use of ground unmanned rover in emergency, to realize an early mapping or to distribute the help. In this context, the Authors have investigated and developed an innovative method for terrestrial rover navigation considering low-cost sensors. The algorithms, implemented in Matlab, start to the acquisition up to the processing phase. The results have shown an impressive performance in terms of positioning accuracies: the maximum difference between the developed solution with respect to the reference coordinates is 60 cm, with a mean value of 38 cm. These results will have a great impact especially for autonomous navigation solutions.

In recent years, numerous natural disaster affected the Italian territory, growing the demand of secure on-field procedures able to accomplish the task of reconnaissance, inspection and survey of instable infrastructure and cultural heritage. During the early-impact there are numerous first response figures, like firefighters, civil protection, geotechnical inspectors and surveyors, that have to operate nearby damaged structure putting at risk their lives in case of possible collapses. Most of these operators work in heavy conditions, so they need tools and procedures that allow them to rush into decisions. These procedures must be rapid, easy to use and must allow the possibility to conduct the task with a certain distance of the instable or not easily accessible structures. This paper deals with the use of tablet devices for rapid close range photogrammetry in post-earthquake scenario, following the close range photogrammetry approach. Starting from images acquired by tablets during speditive surveys, the authors show how is possible to reconstruct the environment starting from data acquired by low cost devices. The case-study considered in this work is the Sant’Agostino Church in Amatrice (Italy), a cultural heritage highly damaged by the 6.0 magnitude earthquake occurred on 24 August 2016 in the central part of Italy.

Modern radio astronomical facilities in the Very High-Frequency band (VHF, 30-300 MHz) consist of large antenna arrays with digital beamforming. The calibration of these instruments requires accurate models of their electromagnetic behavior. Numerical models covering the full telescope field-of-view are nowadays effectively computed by means of full-wave solvers. However, their experimental verification is still an open and challenging task. This letter proposes a verification strategy based on near-field scans. An unmanned aerial vehicle carrying a test source is used to excite the array from a limited set of spatial points, located in the radiating near-field region. The collected data are directly used to verify the telescope electromagnetic model without additional postprocessing. This exploits the capability of full-wave solvers to predict near-field patterns from the same geometrical and electrical model used to compute far-field patterns. The application of the proposed technique to the Low Frequency Array (LOFAR) radio telescope verified the predictions with residuals below 0.5 dB.

Low Frequency Aperture Array (LFAA) systems are opening up new frontiers for ground-based radio astronomical instruments. These instruments require advanced measurement procedures and systems for characterization and calibration of their front-end. For this purpose, a test source mounted on an Unmanned Aerial Vehicle (UAV) has been developed in Italy. This system has proven to be a reliable, accurate, portable and effective tool to characterize LFAA systems as demonstrated during measurement campaigns on two Aperture Array Verification Systems for the LFAA of the SKA in the UK and on a LOFAR station in The Netherlands. This contribution gives an overview of these campaigns, the continuous development of the UAV-based measurement system and the envisaged future improvements.

This paper describes the first results of an image recognition based location (IRBL) for a mobile application focusing on the procedure to generate a database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surroundings is needed. To achieve this objective, a complete 3D survey of two different environments (Bangbae metro station of Seoul and the Electronic and Telecommunications Research Institute (ETRI) building in Daejeon, Republic of Korea) was performed using a LiDAR (Light Detection and Ranging) instrument, and the obtained scans were processed to obtain a spatial model of the environments. From this, two databases of reference images were generated using specific software realised by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allows us to generate synthetically different RGB-D images centred in each scan position in the environment. Later, the external parameters (X, Y, Z, ω, ϕ , and κ) and the range information extracted from the retrieved database images are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper, the survey operations, the approach for generating the RGB-D images, and the IRB strategy are reported. Finally, the analysis of the results and the validation test are described.

In this paper we present the electromagnetic modeling and beam pattern measurements of a 16-elements ultra wideband sparse random test array for the low frequency instrument of the Square Kilometer Array telescope. We discuss the importance of a small array test platform for the development of technologies and techniques towards the final telescope, highlighting the most relevant aspects of its design. We also describe the electromagnetic simulations and modeling work as well as the embedded-element and array pattern measurements using an Unmanned Aerial Vehicle system. The latter are helpful both for the validation of the models and the design as well as for the future instrumental calibration of the telescope thanks to the stable, accurate and strong radio frequency signal transmitted by the UAV. At this stage of the design, these measurements have shown a general agreement between experimental results and numerical data and have revealed the localized effect of un-calibrated cable lengths in the inner side-lobes of the array pattern., This research was supported by the Science & Technology Facilities Council (UK) grant: SKA, ST/M001393/1 and the University of Cambridge, UK. This work is also part of the activities included in the “Advanced calibration techniques for next generation low-frequency radio astronomical arrays” project supported by the 2014 TECNO INAF funds.

The monitoring of air quality parameters is a fundamental requirement for smart cities development and it is of primary importance for the quality of human life. In fact, the knowledge of air quality parameters along the days and in different areas of the city is essential to monitor its behavior and to take some preventive measurements to limit the concentration. Especially in big cities, it is very hard to have widespread updated data about pollutants and air quality parameters since normally only few air quality monitoring stations are available. In Piedmont (Italy), the reference public body for this kind of information is the ARPA (Regional Agency for the Protection of the Environment) which is responsible for the collection and the disclosure of environmental data. However, the number of monitoring stations along the city is limited and they have fixed positions. A solution through mobile sensors would be preferable to have a more comprehensive description of the phenomenon. In this paper, the authors describe the implementation of a new solution based on a mobile system to house environmental air quality and imaging sensors (since also the knowledge of the shape of the environment is a fundamental aspect). In particular, a bicycle is adopted and the paper describes all the analyses involved in the choice of the more appropriate sensors and their evaluation and behavior in a real environment. Finally, the collection and management of the acquired data is analyzed through the implementation of a dedicated GIS (Geographical Information System).

The main aim of the UAV-based measurement campaign conducted at a station of the LOw Frequency ARray (LOFAR) in 2016 was to validate the beam models for the LOFAR stations including mutual coupling, i.e., without assuming identical array elements. Such a validation can be performed as a verification of all the embedded-element patterns over the field-of view and the operative bandwidth. In this paper, the most significant measured results on the low-band elements are reported and discussed.

In the recent years, the authors developed a system for the characterization of the radiation pattern of VHF and UHF antennas by means of a test-source mounted on a micro Unmanned Aerial Vehicle (UAV). So far, the adopted scan strategies typically consisted of two orthogonal straight paths at constant height from ground to obtain E- or H-plane cuts. In this paper, more complex scan strategies are presented to perform two-dimensional pattern measurements over planar and curved surfaces.

Smartphone technology is widespread both in the academy and in the commercial world. Almost every people have today a smartphone in their pocket, that are not only used to call other people but also to share their location on social networks or to plan activities. Today with a smartphone we can compute our position using the sensors settled inside the device that may also include accelerometers, gyroscopes and magnetometers, teslameter, proximity sensors, barometer, and GPS/GNSS chipset. In this chapter we want to analyze the state-of-the-art of the positioning with smartphone technology, considering both outdoor and indoor scenarios. Particular attention will be paid to this last situation, where the accuracy can be improved fusing information coming from more than one sensor. In particular, we will investigate an innovative method of image recognition based (IRB) technology, particularly useful in GNSS denied environment, taking into account the two main problems that arise when the IRB positioning methods are considered: the first one is the optimization of the battery, that implies the minimization of the frame rate, and secondly the latencies due to image processing for visual search solutions, required by the size of the database with the 3D environment images.

Low frequency aperture array technology requires advanced ad-hoc tools for performing antenna and array pattern characterization and instrumental calibration. A micro Unmanned Aerial Vehicle (UAV) mounting a radio-frequency transmitting system developed in Italy has demonstrated to satisfy the challenging characteristics of these tasks. Therefore, a measurement campaign by means of this UAV system has been planned to one Dutch station of the Low Frequency Array (LOFAR) with the main goal to improve the LOFAR antenna and array models. In preparation for this campaign, some initial tests applying the UAV system to one low-frequency antenna of LOFAR were performed in Italy. This contribution describes this measurement session and shows that the measured antenna gain patterns at different frequencies between 40 and 70 MHz agree very well with the electromagnetic models.

The characterization of VHF and UHF antennas is very challenging owing to the very low-operative frequency. This paper presents a novel technique (originally developed for radio-astronomical applications) that can be suitable to measure ground-based aerospace antennas in real installation conditions, with an accurate and cost-effective method which does not require additional infrastructures. The system mainly consists of a micro Unmanned Aerial Vehicle (UAV), equipped with both a RF transmitter and an antenna, properly designed to operate as a far-field artificial test-source. Experimental results on a ground-based antenna at 50 MHz are provided. The extension of this technique to characterize service antennas (telemetry, remote control) onboard aerial vehicles is also reported. A measured pattern example of a 433 MHz telemetry monopole mounted on a commercial UAV is shown.

A UAV-mounted radio-frequency transmitter is proposed as a known reference field source to perform a set of functional tests on aperture arrays. The experimental results obtained on complete prototypes (end-to-end) and sub-assemblies provide good confidence on both amplitude and timing verification.

Air quality is a factor of primary importance for the quality of life. The increase of the pollutants percentage in the air can cause serious problems to the human and environmental health. For this reason it is essential to monitor its values to prevent the consequences of an excessive concentration, to reduce the pollution production or to avoid the contact with major pollutant concentration through the available tools. Some recently developed tools for the monitoring and sharing of the data in an effective system permit to manage the information in a smart way, in order to improve the knowledge of the problem and, consequently, to take preventing measures in favour of the urban air quality and human health. In this paper, the authors describe an innovative solution that implements geomatics sensors (GNSS) and pollutant measurement sensors to develop a low cost sensor for the acquisition of pollutants dynamic data using a mobile platform based on bicycles. The acquired data can be analysed to evaluate the local distribution of pollutant density and shared through web platforms that use standard protocols for an effective smart use.

This paper deals with the electromagnetic modeling of a dipole antenna mounted on a micro Unmanned Aerial Vehicle (UAV). Such a setup has been recently used as a test source to characterize the radiation pattern of fairly large VHF and UHF antennas in their installation conditions i.e. on the ground, with adjacent elements. In this framework, an accurate modeling of the test source is fundamental to distinguish properly its radiation characteristics from those of the antenna under test. The computed results are verified with both reflection coefficient and pattern measurements showing a very good agreement.