单目一次性度量深度对齐用于基于RGB的机器人抓取
Monocular One-Shot Metric-Depth Alignment for RGB-Based Robot Grasping
AI总结：
提出MOMA框架，通过一次性自适应对齐从单张RGB图像恢复度量深度，无需额外训练，在透明物体和真实抓取任务中表现优异。
摘要：
准确的6D物体姿态估计是成功完成机器人夹取和非夹取操作任务的先决条件。目前，用于机器人操作的6D姿态估计通常依赖于基于结构光、飞行时间和立体视觉等技术的深度传感器，这些传感器可能成本高昂，产生噪声输出（与RGB相机相比），并且无法处理透明物体。另一方面，最先进的单目深度估计模型（MDEMs）仅提供在未知尺度和偏移下的仿射不变深度。度量MDEMs在公共数据集上取得了一些成功的零样本结果，但未能泛化。我们提出了一种新颖的框架，单目一次性度量深度对齐（MOMA），通过基于MDEM技术的一次性自适应，从单个RGB图像恢复度量深度。MOMA在相机标定期间执行尺度-旋转-偏移对齐，由稀疏的真实深度点引导，无需额外数据收集或在测试设置上重新训练模型即可实现准确的深度估计。MOMA支持在透明物体上对MDEM进行微调，展示了强大的泛化能力。在桌面双指抓取和基于吸盘的料箱拾取应用上的真实世界实验表明，MOMA在多样化任务中实现了高成功率，证实了其有效性。
英文摘要：
Accurate 6D object pose estimation is a prerequisite for successfully completing robotic prehensile and non-prehensile manipulation tasks. At present, 6D pose estimation for robotic manipulation generally relies on depth sensors based on, e.g., structured light, time-of-flight, and stereo-vision, which can be expensive, produce noisy output (as compared with RGB cameras), and fail to handle transparent objects. On the other hand, state-of-the-art monocular depth estimation models (MDEMs) provide only affine-invariant depths up to an unknown scale and shift. Metric MDEMs achieve some successful zero-shot results on public datasets, but fail to generalize. We propose a novel framework, Monocular One-shot Metric-depth Alignment (MOMA), to recover metric depth from a single RGB image, through a one-shot adaptation building on MDEM techniques. MOMA performs scale-rotation-shift alignments during camera calibration, guided by sparse ground-truth depth points, enabling accurate depth estimation without additional data collection or model retraining on the testing setup. MOMA supports fine-tuning the MDEM on transparent objects, demonstrating strong generalization capabilities. Real-world experiments on tabletop 2-finger grasping and suction-based bin-picking applications show MOMA achieves high success rates in diverse tasks, confirming its effectiveness.
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