Flying Co-Stereo: Enabling Long-Range Aerial Dense Mapping via Collaborative Stereo Vision of Dynamic-Baseline
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915316009795584 |
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| author | Wang, Zhaoying Zuo, Xingxing Dong, Wei |
| author_facet | Wang, Zhaoying Zuo, Xingxing Dong, Wei |
| contents | Lightweight long-range mapping is critical for safe navigation of UAV swarms in large-scale unknown environments. Traditional stereo vision systems with fixed short baselines face limited perception ranges. To address this, we propose Flying Co-Stereo, a cross-agent collaborative stereo vision system that leverages the wide-baseline spatial configuration of two UAVs for long-range dense mapping. Key innovations include: (1) a dual-spectrum visual-inertial-ranging estimator for robust baseline estimation; (2) a hybrid feature association strategy combining deep learning-based cross-agent matching and optical-flow-based intra-agent tracking; (3) A sparse-to-dense depth recovery scheme,refining dense monocular depth predictions using exponential fitting of long-range triangulated sparse landmarks for precise metric-scale mapping. Experiments demonstrate the Flying Co-Stereo system achieves dense 3D mapping up to 70 meters with 2.3%-9.7% relative error, outperforming conventional systems by up to 350% in depth range and 450% in coverage area. The project webpage: https://xingxingzuo.github.io/flying_co_stereo |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00546 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Flying Co-Stereo: Enabling Long-Range Aerial Dense Mapping via Collaborative Stereo Vision of Dynamic-Baseline Wang, Zhaoying Zuo, Xingxing Dong, Wei Robotics Lightweight long-range mapping is critical for safe navigation of UAV swarms in large-scale unknown environments. Traditional stereo vision systems with fixed short baselines face limited perception ranges. To address this, we propose Flying Co-Stereo, a cross-agent collaborative stereo vision system that leverages the wide-baseline spatial configuration of two UAVs for long-range dense mapping. Key innovations include: (1) a dual-spectrum visual-inertial-ranging estimator for robust baseline estimation; (2) a hybrid feature association strategy combining deep learning-based cross-agent matching and optical-flow-based intra-agent tracking; (3) A sparse-to-dense depth recovery scheme,refining dense monocular depth predictions using exponential fitting of long-range triangulated sparse landmarks for precise metric-scale mapping. Experiments demonstrate the Flying Co-Stereo system achieves dense 3D mapping up to 70 meters with 2.3%-9.7% relative error, outperforming conventional systems by up to 350% in depth range and 450% in coverage area. The project webpage: https://xingxingzuo.github.io/flying_co_stereo |
| title | Flying Co-Stereo: Enabling Long-Range Aerial Dense Mapping via Collaborative Stereo Vision of Dynamic-Baseline |
| topic | Robotics |
| url | https://arxiv.org/abs/2506.00546 |