Towards Global Localization using Multi-Modal Object-Instance Re-Identification

2024-09-18

arXiv.org

AI Summary

This paper addresses the challenge of robust object-instance re-identification (ReID) in complex environments and its application to camera global localization and pose estimation. To this end, we propose the first object-level instance ReID framework explicitly designed for localization tasks. Our method introduces a dual-path Transformer architecture that fuses RGB and depth modalities, incorporates cross-modal feature alignment, and employs instance-level contrastive learning. Furthermore, we design an end-to-end geometric verification pipeline driven by ReID outputs. Key contributions include: (i) the first systematic integration of object-instance ReID into camera global localization; (ii) a novel multi-modal aligned dual-path Transformer; and (iii) an interpretable, robust joint ReID–geometric localization paradigm. Evaluated on the TUM RGB-D dataset, our approach achieves 75.18% mAP for instance ReID and 83% localization success rate. Code, pretrained models, and two newly curated RGB-D datasets are publicly released.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

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.

Problem

• Enhancing object-instance ReID for cluttered and varying illumination scenes
• Developing a ReID-based framework for accurate camera localization
• Improving robotic perception with multi-modal RGB-D data integration

Innovation

• Dual-path transformer integrates RGB and depth
• Depth data improves ReID in cluttered scenes
• ReID-based framework enables accurate camera localization