DINO-VO: 注目すべき場所を学習し、状態推定を強化する
DINO-VO: Learning Where to Focus for Enhanced State Estimation
DINO-VOは、適応的なパッチ選択機構を組み込んだエンドツーエンドの単眼視覚オドメトリシステムで、シーン一般化性能を向上させ、多様な環境での追跡精度を達成した。
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著者: Qi Chen, Guanghao Li, Sijia Hu, Xin Gao, Junpeng Ma, Xiangyang Xue, Jian Pu
分類: cs.CV, cs.RO
原文アブストラクト
We present DINO Patch Visual Odometry (DINO-VO), an end-to-end monocular visual odometry system with strong scene generalization. Current Visual Odometry (VO) systems often rely on heuristic feature extraction strategies, which can degrade accuracy and robustness, particularly in large-scale outdoor environments. DINO-VO addresses these limitations by incorporating a differentiable adaptive patch selector into the end-to-end pipeline, improving the quality of extracted patches and enhancing generalization across diverse datasets. Additionally, our system integrates a multi-task feature extraction module with a differentiable bundle adjustment (BA) module that leverages inverse depth priors, enabling the system to learn and utilize appearance and geometric information effectively. This integration bridges the gap between feature learning and state estimation. Extensive experiments on the TartanAir, KITTI, Euroc, and TUM datasets demonstrate that DINO-VO exhibits strong generalization across synthetic, indoor, and outdoor environments, achieving state-of-the-art tracking accuracy.
