Real-time Underwater Place Recognition in Synthetic and Real Environments using Multibeam Sonar and Learning-based Descriptors

One of the biggest challenges in autonomous underwater navigation is the capability of the autonomous underwater vehicle (AUV) to localize itself, since common positioning systems (e.g., GPS or USBL), when available, can be unstable and very noisy. In this paper, we address the problem of place recognition in underwater synthetic and real environments, which is a key component in autonomous localization for robotics and navigation systems. In underwater scenarios, cameras are often subject to water turbidity and low-light conditions, making their use unreliable. Sonar data on the other hand is not affected by these limitations, but its interpretation is more challenging. In this paper we introduce a global descriptor for multibeam sonar images, to be compared with a database of sonar image descriptors acquired at known locations in sparsely structured environments. To enforce the similarity between descriptors computed from nearby poses, we introduce a novel loss that correlates the oriented-Intersection over Union (o-IoU) between pairs of sonar scans with the corresponding distances between their descriptors. A proxy image reconstruction loss has also been integrated for self-supervised adaptation to real data. Preliminary experimental results show that our method is able to localize an AUV in real-time in both synthetic and real environments by training it for localization using only synthetic sonar images.