论文标题
用于声纳图像过滤的有条件gan，并应用于水下占用映射
Conditional GANs for Sonar Image Filtering with Applications to Underwater Occupancy Mapping
论文作者
论文摘要
水下机器人通常依靠像声纳这样的声学传感器来感知周围的环境。但是，这些传感器通常被多种源和类型的噪声淹没，这使得使用原始数据对特征，对象或边界返回的任何有意义的推断非常困难。尽管存在几种传统的处理噪声方法，但它们的成功率并不令人满意。本文介绍了有条件生成的对抗网络（CGAN）的新应用，以训练模型以产生无噪声的声纳图像，从而优于几种常规过滤方法。估计自由空间对于执行主动探索和映射的自主机器人至关重要。因此，与常规方法相比，我们将方法应用于水下占用映射的任务，并显示出卓越的自由和占用空间推断。
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.
