Photo-realistic 3D model based accurate visual positioning system for large-scale indoor spaces

This study presents a novel and reliable visual positioning system (VPS), KR-Net, for kidnap recovery tasks, which predicts an accurate position when a robot is first initiated. KR-Net is based on a hierarchical visual localization method and demonstrates significant robustness in large-scale indoor environments. The proposed VPS utilizes a photo-realistic 3D model to generate a dense database of any camera pose and incorporates a novel global descriptor for indoor spaces, i-GeM, that outperforms existing methods in terms of robustness. Additionally, the proposed combinatorial pooling approach overcomes the limitations of previous single image-based predictions in large-scale indoor environments, allowing for accurate discrimination between similar locations. Extensive evaluations were performed on six large-scale indoor datasets to demonstrate the contributions of each component. To the best of our knowledge, KR-Net is the first system to estimate wake-up positions with a near 100% confidence level within a 1.0m distance error threshold.