Application of Deep Learning-Based Image Registration Techniques in Autonomous Robot Navigation

Autonomous robot navigation is widely applied across various domains, with one of the core challenges being accurate image registration under varying time frames, perspectives, and complex environmental changes. Existing image registration methods address some of these challenges but still face significant limitations, such as insufficient model generalization and low computational efficiency when dealing with highly dynamic and irregular environmental changes. To enhance the accuracy, robustness, and real-time performance of image registration, this paper proposes a deep learning-based image registration technique. The approach comprises three key components: model hypothesis, dataset generation, and overall network design. Through innovative designs such as network-in-network, dual- attention mechanisms, a bidirectional correlation operation in the feature matching layer, and a parameter regression network, this study aims to provide more reliable visual support for autonomous robot navigation.