DarkLoc+: Thermal Image-Based Indoor Localization for Dark Environments With Relative Geometry Constraints

Abstract

Thermal images capture temperature information of the environments instead of texture, making it well suitable for obtaining position in dark environments. Many methods have been proposed to handle RGB images, while thermal image-based localization methods are not well studied. To address it, we propose DarkLoc+, a thermal image-based indoor localization method based on the attention model and relative constraints between images under a learning-based localization framework. To be specific, we utilize self-attention to extract reprehensive features from thermal images and exploit relative constraints to enforce the convolutional neural networks (CNNs) to predict global poses. Relative pose loss(RelLoss)and relative regression loss are designed to work with global poses to constrain the network in feature and pose space simultaneously. We evaluate the proposed method on the public thermal images indoor dataset and our own dataset. The experimental results demonstrate that our method can obtain accurate position information.