用于声呐图像滤波的条件生成对抗网络及其在水下占用栅格建图中的应用
Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping
AI总结：
本文提出利用条件生成对抗网络（cGANs）对声呐图像进行去噪滤波，并将其应用于水下占用栅格建图，实验表明该方法在自由空间与占用空间推断上优于传统滤波方法。
摘要：
水下机器人通常依赖声呐等声学传感器来感知周围环境。然而，这些传感器常常受到多种来源和多种类型噪声的干扰，这使得利用原始数据进行任何有意义的特征、物体或边界回波推断都非常困难。虽然存在若干处理噪声的传统方法，但其成功率并不令人满意。本文提出了一种条件生成对抗网络（cGANs）的新应用，用于训练模型生成无噪声的声呐图像，其性能优于多种传统滤波方法。估计自由空间对于执行主动探索和建图的自主机器人至关重要。因此，我们将该方法应用于水下占用栅格建图任务，并证明与传统方法相比，在自由空间和占用空间推断方面均具有更优表现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
