Fusing Concurrent Orthogonal Wide-aperture Sonar Images for Dense Underwater 3D Reconstruction

TL;DR

This paper introduces a method that fuses orthogonal wide-aperture sonar images from two perspectives to improve dense 3D underwater scene reconstruction, overcoming elevation ambiguity issues.

Contribution

It presents a novel approach combining concurrent orthogonal sonar observations to produce dense 3D reconstructions without strong geometric assumptions.

Findings

Quantitative benchmarks show improved reconstruction accuracy.

Qualitative results demonstrate effective underwater scene reconstruction.

Method successfully handles cluttered underwater environments.

Abstract

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.