  Recent work has achieved dense 3D reconstruction with wide-aperture imaging
sonar using a stereo pair of orthogonally oriented sonars. This allows each
sonar to observe a spatial dimension that the other is missing, without
requiring any prior assumptions about scene geometry. However, this is achieved
only in a small region with overlapping fields-of-view, leaving large regions
of sonar image observations with an unknown elevation angle. Our work aims to
achieve large-scale 3D reconstruction more efficiently using this sensor
arrangement. We propose dividing the world into semantic classes to exploit the
presence of repeating structures in the subsea environment. We use a Bayesian
inference framework to build an understanding of each object class's geometry
when 3D information is available from the orthogonal sonar fusion system, and
when the elevation angle of our returns is unknown, our framework is used to
infer unknown 3D structure. We quantitatively validate our method in a
simulation and use data collected from a real outdoor littoral environment to
demonstrate the efficacy of our framework in the field. Video attachment:
https://www.youtube.com/watch?v=WouCrY9eK4o&t=75s
