VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency

• URL: http://arxiv.org/abs/2602.05508v1
• Date: Thu, 05 Feb 2026 10:07:11 GMT
• Title: VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency
• Authors: Zhuang Xiong, Chen Zhang, Qingshan Xu, Wenbing Tao,
• Abstract summary: VGGT-Motion is a calibration-free SLAM system for efficient global consistency over kilometer-scale trajectories.<n>We first propose a motion-aware submap construction mechanism that uses optical flow to guide adaptive partitioning.<n>We then design an anchor-driven direct Sim(3) registration strategy.<n> Experiments show that VGGT-Motion markedly improves trajectory accuracy and efficiency.
• Score: 28.71501560297241
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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