Underwater Place Recognition in Unknown Environments with Triplet Based Acoustic Image Retrieval

Abstract

Forward-looking sonars (FLS) are perception sensors that are not affected by underwater turbidity. FLS are used in Remotely Operated Vehicles (ROVs) to help them in the tasks of exploration, navigation and region mapping. Besides the advantages of working with acoustic images rather than optical images, the former presents various challenges inherent to their construction. Classic Computer Vision (CV) algorithms do not achieve the same success with acoustic images. Furthermore, data-driven approaches are dictating the state-of-the-art in several tasks that require feature extraction. For example, Convolutional Neural Networks (CNNs) are already been used in several CV problems such as classification, image matching, image retrieval, place recognition and one-shot learning. CNNs are showing promising results for problems with FLS images as well. Unfortunately, there are as not as many public datasets and methods for FLS problems as we have for optical images. Knowing that CNNs are capable of mapping correctly millions of images into thousands of labels, we are proposing a novel framework of feature learning strategy for FLS images. In order to evaluate how well the methods generalize, we selected three different FLS annotated datasets for our experiments. Two of them are real-world FLS images from a harbour environment from different locations. The third is generated from a custom 3D scene integrated with open-source underwater robot simulators. In our experiments, we compared our method with state-of-the-art approaches in an unknown environment achieving superior results.