Bathymetric Surveying with Imaging Sonar Using Neural Volume Rendering

TL;DR

This paper introduces a self-supervised neural volume rendering framework for bathymetric mapping using imaging sonar data, improving accuracy and resolution over existing methods.

Contribution

It presents a novel neural volume rendering approach that jointly models bathymetry and sonar beam patterns, outperforming state-of-the-art techniques.

Findings

Outperforms current state-of-the-art methods in bathymetric reconstruction

Can enhance resolution of low-resolution prior maps

Validated on simulation and real ROV survey data

Abstract

This research addresses the challenge of estimating bathymetry from imaging sonars where the state-of-the-art works have primarily relied on either supervised learning with ground-truth labels or surface rendering based on the Lambertian assumption. In this letter, we propose a novel, self-supervised framework based on volume rendering for reconstructing bathymetry using forward-looking sonar (FLS) data collected during standard surveys. We represent the seafloor as a neural heightmap encapsulated with a parametric multi-resolution hash encoding scheme and model the sonar measurements with a differentiable renderer using sonar volumetric rendering employed with hierarchical sampling techniques. Additionally, we model the horizontal and vertical beam patterns and estimate them jointly with the bathymetry. We evaluate the proposed method quantitatively on simulation and field data collected by remotely operated vehicles (ROVs) during low-altitude surveys. Results show that the proposed method outperforms the current state-of-the-art approaches that use imaging sonars for seabed mapping. We also demonstrate that the proposed approach can potentially be used to increase the resolution of a low-resolution prior map with FLS data from low-altitude surveys.