IMVPR: Implicit BEV-Enhanced Multi-View Aggregation for Visual Place Recognition

Abstract

Visual Place Recognition (VPR) is essential for robotics and autonomous driving, enabling localization by matching current observations with a database of known places. While monocular VPR methods rely on visual features, they are sensitive to environmental changes, and multimodal approaches using LiDAR or radar incur high costs and complexity. Multi-view camera configurations offer a cost-effective alternative by expanding perception range and providing richer structural information. In this work, we propose IMVPR, an implicit BEV-enhanced multi-view place recognition network that achieves consistent and parallel multi-view feature fusion and place descriptors aggregation. Unlike methods that explicitly construct BEV features, we introduce descriptor queries to implicitly represent 3D spatial locations, facilitating spatial point projection-based fusion. A cross-attention mechanism further enables end-to-end multi-view feature aggregation. We evaluate IMVPR on four scenes from the nuScenes dataset, including both in-domain and out-of-domain scenarios, demonstrating its superior accuracy and generalization compared to state-of-the-art methods, including multimodal approaches. Our results highlight the potential of multi-view vision-based methods as a scalable and robust solution for VPR.