Abstract
The aim of the paper was the implementation of low-cost smart sensors for the collection of bathymetric data in shallow water and the development of a 3D modelling methodology for the reconstruction of natural and artificial aquatic scenarios. To achieve the aim, a system called GNSS > Sonar > Phone System (G > S > P Sys) was implemented to synchronise sonar sensors (Deeper Smart Sonars CHIRP+ and Pro+ 2) with an external GNSS receiver (SimpleRTK2B) via smartphone. The bathymetric data collection performances of the G > S > P Sys and the Deeper Smart Sonars were studied through specific tests. Finally, a data-driven method based on a machine learning approach to mapping was developed for the 3D modelling of the bathymetric data produced by the G > S > P Sys. The developed 3D modelling method proved to be flexible, easily implementable and capable of producing models of natural surfaces and submerged artificial structures with centimetre accuracy and precision.
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