SONIC：面向成像声呐的基于位姿监督学习的声呐图像匹配方法
SONIC: Sonar Image Correspondence using Pose Supervised Learning for Imaging Sonars
AI总结：
针对水下SLAM中成像声呐因视角变化导致数据关联困难的问题，提出位姿监督网络SONIC，可生成鲁棒的声呐图像特征匹配，性能显著提升，将公开代码与数据集助力相关研究。
摘要：
本文针对水下同时定位与地图构建（SLAM）中的数据关联难题，提出了一种利用学习特征实现声呐图像匹配的新方法。我们提出了SONIC（SONar Image Correspondence，声呐图像匹配）——一种位姿监督网络，旨在生成能抵抗视角变化的鲁棒特征匹配。水下环境的固有复杂性源于动态且通常受限的能见度条件，视觉感知范围被限制在几米内，且水域常缺乏特征，这使得基于相机的系统在大多数开放水域应用场景中并非最优选择。因此，多波束成像声呐成为感知传感器的首选，但它们也并非没有局限。尽管成像声呐相比相机具备更优的远距离能见度，但其测量结果在不同视角下会呈现差异，这种固有变异性给数据关联带来了巨大挑战，对基于特征的方法而言尤为突出。我们的方法在生成声呐图像匹配点对方面表现出显著更优的性能，这将为实现更准确的回环检测约束和基于声呐的地点识别铺平道路。代码以及模拟和真实世界数据集将被公开，以推动该领域的进一步发展。
英文摘要：
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.
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