Applying an Uncrewed Surface Vessel to Measure Under-Pier Bathymetry

This is the authors' abstract. We don't add key points for this paper.

Determining accurate latitude and longitude positions in GPS-denied environments is a long-standing issue in the fields of navigation and positioning. Much of the ongoing research in these fields centers on costly, evermore sophisticated sensor and algorithm development. Yet, several applications exist, which do not require high levels of precision or investment. This article describes a simple and cost-effective solution developed to map generalized, georeferenced bathymetry underneath piers using an uncrewed surface vessel (USV) with the minimum number of instruments. Under-pier areas are challenging environments constrained by tides, ship movements, varying pier architectures, and sporadic or nonexistent GPS signals. Working within these constraints, we used a small, remotely operated USV with an integrated single-beam sonar system (for depth, z measurements) and geographic positioning system (GPS; for some latitude/longitude, x,y positions) and also used an ultrashort baseline (USBL) acoustic positioning system to determine x,y positions when GPS was denied under the pier. We developed data processing steps to correct the positional and bathymetric estimates and assessed the accuracy of these values. We found that our quality-controlled USBL positions were reasonably precise compared with GPS positions (1.2 and 0.6 m average standard deviation, respectively), although there was also an apparent horizontal offset between USBL and GPS positions that averaged about 3.25 m. However, comparing the sediment volume under piers estimated using this low-cost USV method with that calculated from sidescan sonar-generated bathymetric maps, we found that these volume estimates agreed closely, within <tex-math notation="LaTeX">~</tex-math> 0.6%. This manuscript presents the methods developed, including the approach used to integrate these different data streams, to allow other researchers to collect and process similar data sets in constrained environments.