Characterizing a Debris Field Using Digital Mosaicking and CAD Model Superimposition from Underwater Video

Identifying submerged objects is critical for several disciplines such as marine archaeology and search and rescue. However, identifying objects in underwater searches presents many challenges, particularly if the only data available to analyze is poor-quality video where the videographer did not plan for photogrammetric techniques to be utilized. In this paper, we discuss the use of adaptive sampling of the underwater video to extract sharp still images for stitching and analysis, and creating mosaicked images by identifying and matching local scale-invariant feature transform features using computationally efficient algorithms. Computer aided design models of suspected aircraft components were superimposed, and a feature common in multiple mosaicked images was used to identify a common feature between purported objects to assess goodness of fit. The superimposition method was replicated using landing gear from a reference aircraft and a rope of known dimensions, and favorably compared against the remotely operated vehicle (ROV) analysis results.