MVOFormer: ロバストな単眼視覚オドメトリのためのフロー意味トランスフォーマー
MVOFormer: Flow-Semantic Transformer for Robust Monocular Visual Odometry
単眼視覚オドメトリのための新しいトランスフォーマーフレームワークを提案し、幾何学的動きと意味情報を融合して動的物体を区別し、ゼロショット汎化性能を向上させた。
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著者: Jituo Li, Shunwang Sun, Jialu Zhang, Xinqi Liu, Jinyao Hu, Zhicheng Lu, Sajad Saeedi, Guodong Lu
分類: cs.CV, cs.RO
原文アブストラクト
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.
