HAPOR: Hierarchical-Features Aligned Projection Optimization for Relocalization

Abstract

Locating the 6DoF pose of a camera in a known scene graph is a fundamental problem of SLAM. Hierarchical relocalization methods, which retrieve images first and match feature points later, have been widely studied by scholars for their high accuracy. In this paper, based on hierarchical relocalization, HAPOR (Hierarchical-features Aligned Projection Optimization for Relocalization), an end-to-end relocalization system, is proposed to combine image retrieval and iterative pose optimization. Through an attention mechanism branch, foreground dynamic objects and repeating textures are filtered out. We further design an image retrieval system (GTLGR) in HAPOR and generate an initial pose based on the co-visibility graph for subsequent iterative optimization. In addition, relying on GPS as ground truth for image retrieval training is quite inefficient, thus, we model the common visible area of two camera's view in 3D field, which significantly reduces the training time. Finally, we apply HAPOR to the ORB-SLAM2 system and obtain the state-of-the-art relocalization results.