手術ロボット器具のロバスト追跡：逐次マルチキュー融合とSim-to-Real自己学習
Robust Surgical Robotic Instrument Tracking via Sequential Multi-Cue Fusion and Sim-to-Real Self-Training
合成画像で事前学習したモデルを、ロボット関節状態とEKF・RTSスムーザを用いて実世界動画に自己学習させ、手術器具のキーポイント追跡を高精度化した研究。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Hanyang Hu, Zekai Liang, Florian Richter, Michael C. Yip
分類: cs.RO, cs.CV
原文アブストラクト
Efficient and robust tracking of surgical robotic instruments is important for robot-assisted minimally invasive surgery, yet remains challenging due to the complexity of surgical scenes and the unconventional geometry of surgical instruments. Keypoint-based approaches are efficient, but their performance depends on reliable feature detection. Improving these detectors with real-world supervision is difficult because accurate real-world annotations are costly to obtain at scale. To address this limitation, we introduce a tracker-guided self-training framework that adapts a model pretrained on synthetic images to unlabeled real-world videos. Given measured robot joint states, an uncertainty-aware EKF recursively corrects the instrument pose and the observable joint angles by comparing projected model features with detected keypoints, shaft boundaries, and mask-derived cues. An RTS smoother subsequently refines the resulting trajectory, which is projected into pseudo-labels for fine-tuning the feature detector without laborious pose annotations. Experiments on real-world videos demonstrate consistent improvements from self-training across all evaluated keypoint metrics, and the resulting model outperforms prior approaches in both accuracy and runtime. The code and data will be released upon publication.
