无需标记和训练，实时估算机械臂末端位姿
Foundation Feature-Driven Online End-Effector Pose Estimation: A Marker-Free and Learning-Free Approach
- 利用预训练视觉特征匹配CAD模型与图像的2D-3D对应点
- 通过多历史关键帧优化解决部分遮挡与对称性问题
- 跨机器人、跨末端执行器零样本泛化，适合实际部署
相机空间与机器人空间之间的精确变换估计至关重要。传统标记法手眼标定需离线采集图像，难以实现在线自标定；现有基于学习的方法虽支持在线校准，但泛化能力差且要求机器人完全可见。本文提出一种无训练、跨末端通用的在线末端位姿估计算法（FEEPE），受基础模型零样本泛化启发，利用预训练视觉特征提取由CAD模型与目标图像生成的2D-3D对应关系，通过PnP算法实现6自由度姿态估计。针对部分观测与对称性带来的歧义，引入多历史关键帧增强的姿态优化算法，利用时间信息提升精度。实验表明，相比传统手眼标定，FEEPE实现无标记在线标定；不同于传统机器人位姿估计，其具备跨机器人与末端执行器的无训练泛化能力，展现出卓越的灵活性与性能。
原文摘要 · Abstract (English)
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.
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