SONIC: Sonar Image Correspondence using Pose Supervised Learning for Imaging Sonars
This addresses data association for underwater SLAM using imaging sonars, which is crucial for autonomous underwater vehicles but faces challenges from viewpoint variability, representing a novel method for a known bottleneck.
The paper tackles the challenging problem of data association for underwater SLAM by introducing SONIC, a pose-supervised network for sonar image correspondence that yields robust feature matching across viewpoint variations, demonstrating significantly better performance for generating correspondences.
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.