Visual Place Recognition via Local Affine Preserving Matching

Abstract

Visual Place Recognition (VPR) is a crucial component for long-term mobile robot autonomy. In this paper, we exploit a coarse-to-fine paradigm to recognize places. In particular, we first select candidate frames for each query image, and then check the spatial geometric relationship between the query and its candidate frames to determine the final place match. In the coarse match stage, we employ the deep learning network to extract global features that encode semantic information of images, then by comparing the similarity between features to obtain a candidate list of the query place. In the fine match stage, we propose an effective and efficient feature matching algorithm for real-time geometrical verification of candidate places, termed as local affine preserving matching (LAP). Extensive experimental results demonstrate that our LAP can significantly promote the VPR performance, and the proposed overall VPR method can achieve much better performance over the current state-of-the-art approaches.