论文标题
评估GAN以增强机器人技术的相机模拟
Evaluating a GAN for enhancing camera simulation for robotics
论文作者
论文摘要
鉴于生成对抗网络（GAN）的多功能性，我们试图了解使用现有的GAN增强模拟图像并减少SIM卡对真实差距而获得的好处。我们在模拟机器人性能和基于图像的感知的背景下进行分析。具体而言，我们量化了GAN在机器人技术中减少图像感知中SIM卡之间差异的能力。使用语义细分，我们使用名义上和增强的城市环境模拟分析了训练和测试中的SIM对差异差异。作为次要应用，我们考虑使用GAN来增强室内环境。对于此应用，对象检测用于分析训练和测试的增强。结果提出的结果量化了使用GAN时SIM到真实差距的减少，并说明了其使用的好处。
Given the versatility of generative adversarial networks (GANs), we seek to understand the benefits gained from using an existing GAN to enhance simulated images and reduce the sim-to-real gap. We conduct an analysis in the context of simulating robot performance and image-based perception. Specifically, we quantify the GAN's ability to reduce the sim-to-real difference in image perception in robotics. Using semantic segmentation, we analyze the sim-to-real difference in training and testing, using nominal and enhanced simulation of a city environment. As a secondary application, we consider use of the GAN in enhancing an indoor environment. For this application, object detection is used to analyze the enhancement in training and testing. The results presented quantify the reduction in the sim-to-real gap when using the GAN, and illustrate the benefits of its use.
