Exploiting Deep Matching and Underwater Terrain Images to Improve Underwater Localization Accuracy

Abstract

Underwater terrain image (UTI) matching provides a new idea for terrain-aided localization technology. However, the diversity of UTIs makes it very difficult to accurately match real-time terrain images (RTTIs) with reference terrain images (RTIs). To improve the localization accuracy of UTIs, this letter describes how UTI matching can be modeled as a deep learning problem based on supervised algorithms. Specifically, we propose a hierarchical matching strategy for the diverse features of UTIs by combining the deep and shallow feature characteristics of a convolutional neural network (CNN) to improve the localization accuracy. We also propose a sample-generation strategy based on candidate area quality index classification, and develop a data enhancement method by simulating the underwater terrain environment to overcome the lack of network training data. Simulations prove that the proposed method achieves accurate and stable localization results.