A Generative Model of Underwater Images for Active Landmark Detection and Docking Underwater active landmarks (UALs) are widely used for shortrange underwater navigation in underwater robotics tasks. Detection of UALs is challenging due to large variance of underwater illumination, water quality and change of camera viewpoint. Moreover, improvement of detection accuracy relies upon statistical diversity of images used to train detection models. We propose a generative adversarial network, called Tanktofield GAN (T2FGAN), to learn generative models of underwater images, and use the learned models for data augmentation to improve detection accuracy. To this end, first a T2FGAN is trained using images of UALs captured in a tank. Then, the learned model of the T2FGAN is used to generate images of UALs according to different water quality, illumination, pose and landmark configurations (WIPCs). In experimental analyses, we first explore statistical properties of images of UALs generated by T2FGAN under various WIPCs for active landmark detection. Then, we use the generated images for training detection algorithms. Experimental results show that training detection algorithms using the generated images can improve detection accuracy. In field experiments, underwater docking tasks are successfully performed in a lake by employing detection models trained on datasets generated by T2FGAN.

Wide Aperture Imaging Sonar Reconstruction using Generative Models In this paper we propose a new framework for reconstructing underwater surfaces from wide aperture imaging sonar sequences. We demonstrate that when the leading object edge in each sonar image can be accurately triangulated in 3D, the remaining surface may be xe2x80x9cfilled inxe2x80x9d using a generative sensor model. This process generates a full threedimensional point cloud for each image in the sequence. We propose integrating these surface measurements into a cohesive global map using a truncated signed distance field (TSDF) to fuse the point clouds generated by each image. This allows for reconstructing surfaces with significantly fewer sonar images and viewpoints than previous methods. The proposed method is evaluated by reconstructing a mockup piling structure and a real world underwater piling, in a test tank environment and in the field, respectively. Our surface reconstructions are quantitatively compared to groundtruth models and are shown to be more accurate than previous stateoftheart algorithms.