Towards Global Localization using Multi-Modal Object-Instance Re-Identification
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908344526045184 |
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| author | Chavan, Aneesh Agrawal, Vaibhav Bhat, Vineeth Chittawar, Sarthak Srivastava, Siddharth Arora, Chetan Krishna, K Madhava |
| author_facet | Chavan, Aneesh Agrawal, Vaibhav Bhat, Vineeth Chittawar, Sarthak Srivastava, Siddharth Arora, Chetan Krishna, K Madhava |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12002 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Global Localization using Multi-Modal Object-Instance Re-Identification Chavan, Aneesh Agrawal, Vaibhav Bhat, Vineeth Chittawar, Sarthak Srivastava, Siddharth Arora, Chetan Krishna, K Madhava Robotics Computer Vision and Pattern Recognition 68T40 I.2.9; I.2.10 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. |
| title | Towards Global Localization using Multi-Modal Object-Instance Re-Identification |
| topic | Robotics Computer Vision and Pattern Recognition 68T40 I.2.9; I.2.10 |
| url | https://arxiv.org/abs/2409.12002 |