PanoVPR: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows across the Panoramic View

Visual place recognition has received increasing attention in recent years as a key technology in autonomous driving and robotics. The current mainstream approaches use either the perspective view retrieval perspective view (P2P) paradigm or the equirectangular image retrieval equirectangular image (E2E) paradigm. However, a natural and practical idea is that users only have consumer-grade pinhole cameras to obtain query perspective images and retrieve them in panoramic database images from map providers. To this end, we propose PanoVPR, a sliding-window-based perspective-to-equirectangular (P2E) visual place recognition framework, which eliminates feature truncation caused by hard cropping by sliding windows over the whole equirectangular image and computing and comparing feature descriptors between windows. In addition, this unified framework allows for directly transferring the network structure used in perspective-to-perspective (P2P) methods without modification. To facilitate training and evaluation, we derive the pitts250k-P2E dataset from the pitts250k and achieve promising results, and we also establish a P2E dataset in a real-world scenario by a mobile robot platform, which we refer to YQ360. Code and datasets will be made available at https://github.com/zafirshi/PanoVPR.