  We propose a novel approach to handling the ambiguity in elevation angle
associated with the observations of a forward looking multi-beam imaging sonar,
and the challenges it poses for performing an accurate 3D reconstruction. We
utilize a pair of sonars with orthogonal axes of uncertainty to independently
observe the same points in the environment from two different perspectives, and
associate these observations. Using these concurrent observations, we can
create a dense, fully defined point cloud at every time-step to aid in
reconstructing the 3D geometry of underwater scenes. We will evaluate our
method in the context of the current state of the art, for which strong
assumptions on object geometry limit applicability to generalized 3D scenes. We
will discuss results from laboratory tests that quantitatively benchmark our
algorithm's reconstruction capabilities, and results from a real-world, tidal
river basin which qualitatively demonstrate our ability to reconstruct a
cluttered field of underwater objects.
