Exploring Emerging Trends and Research Opportunities in Visual Place Recognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915069633232896 |
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| author | Gasteratos, Antonios Tsintotas, Konstantinos A. Fischer, Tobias Aloimonos, Yiannis Milford, Michael |
| author_facet | Gasteratos, Antonios Tsintotas, Konstantinos A. Fischer, Tobias Aloimonos, Yiannis Milford, Michael |
| contents | Visual-based recognition, e.g., image classification, object detection, etc., is a long-standing challenge in computer vision and robotics communities. Concerning the roboticists, since the knowledge of the environment is a prerequisite for complex navigation tasks, visual place recognition is vital for most localization implementations or re-localization and loop closure detection pipelines within simultaneous localization and mapping (SLAM). More specifically, it corresponds to the system's ability to identify and match a previously visited location using computer vision tools. Towards developing novel techniques with enhanced accuracy and robustness, while motivated by the success presented in natural language processing methods, researchers have recently turned their attention to vision-language models, which integrate visual and textual data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11481 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Emerging Trends and Research Opportunities in Visual Place Recognition Gasteratos, Antonios Tsintotas, Konstantinos A. Fischer, Tobias Aloimonos, Yiannis Milford, Michael Computer Vision and Pattern Recognition Robotics Visual-based recognition, e.g., image classification, object detection, etc., is a long-standing challenge in computer vision and robotics communities. Concerning the roboticists, since the knowledge of the environment is a prerequisite for complex navigation tasks, visual place recognition is vital for most localization implementations or re-localization and loop closure detection pipelines within simultaneous localization and mapping (SLAM). More specifically, it corresponds to the system's ability to identify and match a previously visited location using computer vision tools. Towards developing novel techniques with enhanced accuracy and robustness, while motivated by the success presented in natural language processing methods, researchers have recently turned their attention to vision-language models, which integrate visual and textual data. |
| title | Exploring Emerging Trends and Research Opportunities in Visual Place Recognition |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2411.11481 |