Autonomous navigation in underwater environments presents challenges due to factors such as light absorption and water turbidity, limiting the effectiveness of optical sensors. Sonar systems are commonly used for perception in underwater operations as they are unaffected by these limitations. Traditional computer vision algorithms are less effective when applied to sonar-generated acoustic images, while convolutional neural networks (CNNs) typically require large amounts of labeled training data that are often unavailable or difficult to acquire. To this end, we propose a novel compact deep sonar descriptor pipeline that can generalize to real scenarios while being trained exclusively on synthetic data. Our architecture is based on a ResNet18 back-end and a properly parameterized random Gaussian projection layer, whereas input sonar data is enhanced with standard ad-hoc normalization/prefiltering techniques. A customized synthetic data generation procedure is also presented. The proposed method has been evaluated extensively using both synthetic and publicly available real data, demonstrating its effectiveness compared to state-of-the-art methods.

翻译：水下环境中的自主导航面临光吸收和水体浊度等因素带来的挑战，这限制了光学传感器的有效性。声纳系统因不受这些限制影响，常被用于水下作业中的感知任务。传统计算机视觉算法在应用于声纳生成的声学图像时效果较差，而卷积神经网络（CNN）通常需要大量标记训练数据，但这些数据往往难以获取或采集困难。为此，我们提出一种新型紧凑型深度声纳描述符管道，该管道在仅使用合成数据训练的情况下，能够泛化至真实场景。我们的架构基于ResNet18后端和参数化适当的随机高斯投影层，同时通过标准适配归一化/预滤波技术对输入声纳数据进行增强。此外，还提出了一种定制化的合成数据生成流程。该方法经合成数据及公开真实数据的广泛评估，证明其相较现有最优方法具有显著有效性。