Keyframe-Based Feed-Forward Visual Odometry

• URL: http://arxiv.org/abs/2601.16020v1
• Date: Thu, 22 Jan 2026 14:45:42 GMT
• Title: Keyframe-Based Feed-Forward Visual Odometry
• Authors: Weichen Dai, Wenhan Su, Da Kong, Yuhang Ming, Wanzeng Kong,
• Abstract summary: Current foundation model based methods typically process raw image sequences indiscriminately.<n>We propose a novel feed-forward VO method that employs reinforcement learning to derive an adaptive visual policy in a data-driven manner.<n> Experimental results demonstrate that the proposed method achieves consistent and substantial improvements over state-of-the-art feed-forward VO methods.
• Score: 13.646685343885556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of visual foundation models has revolutionized visual odometry~(VO) and SLAM, enabling pose estimation and dense reconstruction within a single feed-forward network. However, unlike traditional pipelines that leverage keyframe methods to enhance efficiency and accuracy, current foundation model based methods, such as VGGT-Long, typically process raw image sequences indiscriminately. This leads to computational redundancy and degraded performance caused by low inter-frame parallax, which provides limited contextual stereo information. Integrating traditional geometric heuristics into these methods is non-trivial, as their performance depends on high-dimensional latent representations rather than explicit geometric metrics. To bridge this gap, we propose a novel keyframe-based feed-forward VO. Instead of relying on hand-crafted rules, our approach employs reinforcement learning to derive an adaptive keyframe policy in a data-driven manner, aligning selection with the intrinsic characteristics of the underlying foundation model. We train our agent on TartanAir dataset and conduct extensive evaluations across several real-world datasets. Experimental results demonstrate that the proposed method achieves consistent and substantial improvements over state-of-the-art feed-forward VO methods.

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