Self-Supervised Camera Relocalization With Hierarchical Fern Encoding.

Visual relocalization plays a crucial role in unmanned systems, which automatically estimates the camera pose in a known scene with the input visual information. However, existing visual relocalization methods encounter challenges in relocating images with significant viewpoint variations, and they often lack the ability to self-identify the precision of the relocated camera pose, leading to low accuracy and recall. In this study, we propose a novel visual relocalization method that leverages a hierarchical image fern encoding and matching technique to accurately relocalize under large viewpoint differences. Furthermore, we introduce a self-supervised pose optimization module that self-identify the precision of the relocated camera poses and refines the false or inaccurate poses via a looped correction strategy. To identify the precision without ground truth, we translate the problem of precision identification into the classification of precision range and design a multi-support vector machine (SVM) module to classify the precision range of a relocated camera pose by the output values of the iterative closest point (ICP). We evaluate our proposed method on two widely used datasets, 7-scenes and 12-scenes, and show that our method substantially reduces false positives in camera relocalization. In particular, our method achieves more than a 20% improvement in recall with 1 cm/1° accuracy compared to the state-of-the-art methods.