OpenMulti: Open-Vocabulary Instance-Level Multi-Agent Distributed Implicit Mapping
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916928056983552 |
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| author | Dou, Jianyu Deng, Yinan Wang, Jiahui Tang, Xingsi Yang, Yi Yue, Yufeng |
| author_facet | Dou, Jianyu Deng, Yinan Wang, Jiahui Tang, Xingsi Yang, Yi Yue, Yufeng |
| contents | Multi-agent distributed collaborative mapping provides comprehensive and efficient representations for robots. However, existing approaches lack instance-level awareness and semantic understanding of environments, limiting their effectiveness for downstream applications. To address this issue, we propose OpenMulti, an open-vocabulary instance-level multi-agent distributed implicit mapping framework. Specifically, we introduce a Cross-Agent Instance Alignment module, which constructs an Instance Collaborative Graph to ensure consistent instance understanding across agents. To alleviate the degradation of mapping accuracy due to the blind-zone optimization trap, we leverage Cross Rendering Supervision to enhance distributed learning of the scene. Experimental results show that OpenMulti outperforms related algorithms in both fine-grained geometric accuracy and zero-shot semantic accuracy. In addition, OpenMulti supports instance-level retrieval tasks, delivering semantic annotations for downstream applications. The project website of OpenMulti is publicly available at https://openmulti666.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenMulti: Open-Vocabulary Instance-Level Multi-Agent Distributed Implicit Mapping Dou, Jianyu Deng, Yinan Wang, Jiahui Tang, Xingsi Yang, Yi Yue, Yufeng Robotics Multi-agent distributed collaborative mapping provides comprehensive and efficient representations for robots. However, existing approaches lack instance-level awareness and semantic understanding of environments, limiting their effectiveness for downstream applications. To address this issue, we propose OpenMulti, an open-vocabulary instance-level multi-agent distributed implicit mapping framework. Specifically, we introduce a Cross-Agent Instance Alignment module, which constructs an Instance Collaborative Graph to ensure consistent instance understanding across agents. To alleviate the degradation of mapping accuracy due to the blind-zone optimization trap, we leverage Cross Rendering Supervision to enhance distributed learning of the scene. Experimental results show that OpenMulti outperforms related algorithms in both fine-grained geometric accuracy and zero-shot semantic accuracy. In addition, OpenMulti supports instance-level retrieval tasks, delivering semantic annotations for downstream applications. The project website of OpenMulti is publicly available at https://openmulti666.github.io/. |
| title | OpenMulti: Open-Vocabulary Instance-Level Multi-Agent Distributed Implicit Mapping |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.01228 |