UniLoc: Towards Universal Place Recognition Using Any Single Modality
This work addresses the need for flexible and efficient place recognition systems for robotics and autonomous navigation, though it is incremental as it builds on existing contrastive learning methods.
The authors tackled the problem of universal place recognition by developing UniLoc, a model that works with any single query modality (natural language, image, or point cloud), achieving superior performance in cross-modal settings and competitive results in uni-modal scenarios on the KITTI-360 dataset.
To date, most place recognition methods focus on single-modality retrieval. While they perform well in specific environments, cross-modal methods offer greater flexibility by allowing seamless switching between map and query sources. It also promises to reduce computation requirements by having a unified model, and achieving greater sample efficiency by sharing parameters. In this work, we develop a universal solution to place recognition, UniLoc, that works with any single query modality (natural language, image, or point cloud). UniLoc leverages recent advances in large-scale contrastive learning, and learns by matching hierarchically at two levels: instance-level matching and scene-level matching. Specifically, we propose a novel Self-Attention based Pooling (SAP) module to evaluate the importance of instance descriptors when aggregated into a place-level descriptor. Experiments on the KITTI-360 dataset demonstrate the benefits of cross-modality for place recognition, achieving superior performance in cross-modal settings and competitive results also for uni-modal scenarios. Our project page is publicly available at https://yan-xia.github.io/projects/UniLoc/.