Underwater robots typically rely on acoustic sensors like sonar to perceive their surroundings. However, these sensors are often inundated with multiple sources and types of noise, which makes using raw data for any meaningful inference with features, objects, or boundary returns very difficult. While several conventional methods of dealing with noise exist, their success rates are unsatisfactory. This paper presents a novel application of conditional Generative Adversarial Networks (cGANs) to train a model to produce noise-free sonar images, outperforming several conventional filtering methods. Estimating free space is crucial for autonomous robots performing active exploration and mapping. Thus, we apply our approach to the task of underwater occupancy mapping and show superior free and occupied space inference when compared to conventional methods.

翻译：水下机器人通常依赖声纳等声学传感器感知周围环境。然而，这些传感器常受多种来源和类型的噪声干扰，使得基于原始数据对特征、物体或边界回波进行有效推理变得极为困难。尽管存在多种传统噪声处理方法，但其成功率仍不理想。本文提出了一种条件生成对抗网络（cGANs）的新应用，通过训练模型生成无噪声声纳图像，其性能优于多种传统滤波方法。对于执行主动探索与地图构建的自主机器人而言，自由空间估计至关重要。因此，我们将该方法应用于水下占用地图构建任务，并展示了相较于传统方法更优的自由与占用空间推理能力。