VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917250907242496 |
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| author | Xiong, Zhuang Zhang, Chen Xu, Qingshan Tao, Wenbing |
| author_facet | Xiong, Zhuang Zhang, Chen Xu, Qingshan Tao, Wenbing |
| contents | Despite recent progress in calibration-free monocular SLAM via 3D vision foundation models, scale drift remains severe on long sequences. Motion-agnostic partitioning breaks contextual coherence and causes zero-motion drift, while conventional geometric alignment is computationally expensive. To address these issues, we propose VGGT-Motion, a calibration-free SLAM system for efficient and robust global consistency over kilometer-scale trajectories. Specifically, we first propose a motion-aware submap construction mechanism that uses optical flow to guide adaptive partitioning, prune static redundancy, and encapsulate turns for stable local geometry. We then design an anchor-driven direct Sim(3) registration strategy. By exploiting context-balanced anchors, it achieves search-free, pixel-wise dense alignment and efficient loop closure without costly feature matching. Finally, a lightweight submap-level pose graph optimization enforces global consistency with linear complexity, enabling scalable long-range operation. Experiments show that VGGT-Motion markedly improves trajectory accuracy and efficiency, achieving state-of-the-art performance in zero-shot, long-range calibration-free monocular SLAM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05508 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency Xiong, Zhuang Zhang, Chen Xu, Qingshan Tao, Wenbing Computer Vision and Pattern Recognition Despite recent progress in calibration-free monocular SLAM via 3D vision foundation models, scale drift remains severe on long sequences. Motion-agnostic partitioning breaks contextual coherence and causes zero-motion drift, while conventional geometric alignment is computationally expensive. To address these issues, we propose VGGT-Motion, a calibration-free SLAM system for efficient and robust global consistency over kilometer-scale trajectories. Specifically, we first propose a motion-aware submap construction mechanism that uses optical flow to guide adaptive partitioning, prune static redundancy, and encapsulate turns for stable local geometry. We then design an anchor-driven direct Sim(3) registration strategy. By exploiting context-balanced anchors, it achieves search-free, pixel-wise dense alignment and efficient loop closure without costly feature matching. Finally, a lightweight submap-level pose graph optimization enforces global consistency with linear complexity, enabling scalable long-range operation. Experiments show that VGGT-Motion markedly improves trajectory accuracy and efficiency, achieving state-of-the-art performance in zero-shot, long-range calibration-free monocular SLAM. |
| title | VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.05508 |