基盤モデル特徴駆動によるオンラインエンドエフェクタ姿勢推定：マーカーフリー・学習フリー手法
Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach
事前学習済み視覚特徴とCADモデルから2D-3D対応を求め、PnPと時系列最適化でエンドエフェクタの6D姿勢を学習なし・マーカーなしでオンライン推定する手法を提案。
著者: Tianshu Wu, Jiyao Zhang, Shiqian Liang, Zhengxiao Han, Hao Dong
分類: cs.RO, cs.CV
原文アブストラクト
Accurate transformation estimation between camera space and robot space is essential. Traditional methods using markers for hand-eye calibration require offline image collection, limiting their suitability for online self-calibration. Recent learning-based robot pose estimation methods, while advancing online calibration, struggle with cross-robot generalization and require the robot to be fully visible. This work proposes a Foundation feature-driven online End-Effector Pose Estimation (FEEPE) algorithm, characterized by its training-free and cross end-effector generalization capabilities. Inspired by the zero-shot generalization capabilities of foundation models, FEEPE leverages pre-trained visual features to estimate 2D-3D correspondences derived from the CAD model and target image, enabling 6D pose estimation via the PnP algorithm. To resolve ambiguities from partial observations and symmetry, a multi-historical key frame enhanced pose optimization algorithm is introduced, utilizing temporal information for improved accuracy. Compared to traditional hand-eye calibration, FEEPE enables marker-free online calibration. Unlike robot pose estimation, it generalizes across robots and end-effectors in a training-free manner. Extensive experiments demonstrate its superior flexibility, generalization, and performance.
