Towards Global Localization using Multi-Modal Object-Instance Re-Identification
It addresses object-instance ReID for robotic perception, which is underexplored but incremental as it builds on existing ReID methods by adding depth data.
The paper tackles robust object-instance re-identification (ReID) for tasks like autonomous exploration by proposing a dual-path transformer that integrates RGB and depth data, achieving a mAP of 75.18 for ReID and an 83% success rate for localization on TUM-RGBD.
Re-identification (ReID) is a critical challenge in computer vision, predominantly studied in the context of pedestrians and vehicles. However, robust object-instance ReID, which has significant implications for tasks such as autonomous exploration, long-term perception, and scene understanding, remains underexplored. In this work, we address this gap by proposing a novel dual-path object-instance re-identification transformer architecture that integrates multimodal RGB and depth information. By leveraging depth data, we demonstrate improvements in ReID across scenes that are cluttered or have varying illumination conditions. Additionally, we develop a ReID-based localization framework that enables accurate camera localization and pose identification across different viewpoints. We validate our methods using two custom-built RGB-D datasets, as well as multiple sequences from the open-source TUM RGB-D datasets. Our approach demonstrates significant improvements in both object instance ReID (mAP of 75.18) and localization accuracy (success rate of 83% on TUM-RGBD), highlighting the essential role of object ReID in advancing robotic perception. Our models, frameworks, and datasets have been made publicly available.