A wide range of industrial applications benefits from the accessibility of image-based techniques for three-dimensional modelling of different multi-scale objects. In the last decade, along with the technological progress mainly achieved with the use of Unmanned Aerial Vehicles (UAVs), there has been an exponential growth of software platforms enabled to return photogrammetric products. On the other hand, the different levels of final product accuracy resulting from the adoption of different processing approaches in various softwares have not yet been fully understood. To date, there is no validation analysis in literature focusing on the comparability of such products, not even in relation to the use of workflows commonly allowed inside various software platforms. The lack of detailed information about the algorithms implemented in the licensed platforms makes the whole interpretation even more complex. This work therefore aims to provide a comparative evaluation of three photogrammetric softwares commonly used in the industrial field, in order to obtain coherent, if not exactly congruent results. After structuring the overall processing workflow, the processing pipelines were accurately parameterized to make them comparable in both licensed and open-source softwares. For the best interpretation of the results derived from the generation of point clouds processed by the same image dataset, the obtainable values of root-mean-square error (RMSE) were analyzed, georeferencing models as the number of GCPs varied. The tests carried out aimed at investigating the elements shared by the platforms tested, with the purpose of supporting future studies to define a unique index for the accuracy of final products.

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The aim of this work is to provide a review of the main indoor positioning methodologies, in order to evidence their strengths and weaknesses, and explore the potential of the integration in an Unmanned Ground Vehicle built for tunnel monitoring purposes. A robotic platform, named Bulldog, has been designed and assembled by Sipal S.p.a., with the support of the research group Applied Geomatic laboratory (AGlab) of the Politecnico di Bari, in the definition of the data processing pipeline. Preliminary results show that the integration of indoor positioning techniques in the Bulldog platform represents an important advance for accurate monitoring and analysis of a tunnel during the construction stage, allowing a fast and reliable survey of the indoor environment and requiring, at this prototypal stage of development, only a remote supervision by the operator. Expected improvements will allow to carry out tunnel monitoring activities in a fully autonomous mode, bringing benefit for the safety of people involved in the construction works and the accuracy of the acquired dataset.