In this paper, we address the challenging problem of data association for
underwater SLAM through a novel method for sonar image correspondence using
learned features. We introduce SONIC (SONar Image Correspondence), a
pose-supervised network designed to yield robust feature correspondence capable
of withstanding viewpoint variations. The inherent complexity of the underwater
environment stems from the dynamic and frequently limited visibility
conditions, restricting vision to a few meters of often featureless expanses.
This makes camera-based systems suboptimal in most open water application
scenarios. Consequently, multibeam imaging sonars emerge as the preferred
choice for perception sensors. However, they too are not without their
limitations. While imaging sonars offer superior long-range visibility compared
to cameras, their measurements can appear different from varying viewpoints.
This inherent variability presents formidable challenges in data association,
particularly for feature-based methods. Our method demonstrates significantly
better performance in generating correspondences for sonar images which will
pave the way for more accurate loop closure constraints and sonar-based place
recognition. Code as well as simulated and real-world datasets will be made
public to facilitate further development in the field.

通过使用学习到的特征进行声纳图像对应关系，本文针对水下 SLAM 中的数据关联问题提出了一种新颖的方法。我们介绍了 SONIC（SONar Image Correspondence），一个通过姿态监督的网络，旨在生成能够抵御视角变化的强大特征对应关系。该方法在为声纳图像生成对应关系方面表现出显著的优异性能，为更准确的闭环约束和基于声纳的地点识别铺平了道路。为促进该领域的进一步发展，我们将提供代码以及模拟和真实世界的数据集。