MVOFormer: Flow-Semantic Transformer for Robust Monocular Visual Odometry

Abstract:Monocular visual odometry (MVO) is foundational to autonomous navigation and robotic localization. However, existing learning-based MVO approaches often struggle with either a lack of interpretable, complementary features or overly complex multi-stage architectures. These limitations inherently restrict their robustness and cross-domain generalization. In this work, we propose MVOFormer, a novel transformer framework for robust monocular visual odometry. Our architecture features a Flow-Semantic Dual Branch Encoder that synergizes dense geometric motion cues with object-centric semantic priors, explicitly distinguishing static structures from dynamic distractors. These representations are then fused by an Iterative Multimodal Decoder, enabling coarse-to-fine pose refinement while dynamically suppressing attention on unreliable regions. Extensive evaluations demonstrate that, without any target-domain fine-tuning, MVOFormer achieves superior zero-shot generalization and robustness, significantly outperforming prior learning-based frame-to-frame methods across diverse benchmarks including TartanAir, KITTI, TUM-RGBD, and ETH3D-SLAM.

8 pages, 6 figures. Accepted for publication in IEEE Robotics and Automation Letters (RA-L)

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2606.16474 [cs.CV]

(or arXiv:2606.16474v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.16474 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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