Multimodal Image Registration for GPS-denied UAV Navigation Based on Disentangled Representations

Visual navigation plays an important role for Unmanned Aerial Vehicles(UAVs). In some applications, the landmark image and the real-time image may be heterogeneous, like near-infrared and visible images. In this work, we propose a multimodal image registration method to deal with near-infrared and visible images so that it can be applied to visual navigation system for the localization of UAVs in GPS-denied environments. At first, a new feature extraction strategy is developed to embed different modalities of images into the common feature space based on disentangled representations. Such common space is independent of the image modality, and this can eliminate the modality differences. Meanwhile, an intensity loss is introduced to measure the similarity of mono-modal images. In the proposed method, we can directly predict the transformation parameters and thus accelerates the localization of UAV s. Extensive experiments on synthetic datasets are conducted to demonstrate the validity of our method, and the experimental results show that the proposed method can effectively improve the localization accuracy.