无需标记物和训练，自动完成机械臂与相机的标定
EasyHeC++: Fully Automatic Hand-Eye Calibration with Pretrained Image Models
- 利用预训练图像模型初始化相机位姿，再通过可微渲染优化
- 在多种机械臂和相机设置下，合成与真实数据集均表现优异
- 适合希望快速部署、免人工干预标定的机器人研发者
手眼标定在机器人领域至关重要，直接影响抓取与操作效率。本文提出 EasyHeC++，首个无需标记物、无需训练、全自动的手眼标定框架。该方法采用两步流程：首先借助预训练图像模型，通过采样或特征匹配初始化相机位姿；随后利用可微渲染进行位姿优化。大量实验表明，该系统在多种机械臂和相机配置下的合成与真实数据集上均表现出卓越精度。项目页面：https://ootts.github.io/easyhec_plus。
原文摘要 · Abstract (English)
Hand-eye calibration plays a fundamental role in robotics by directly influencing the efficiency of critical operations such as manipulation and grasping. In this work, we present a novel framework, EasyHeC++, designed for fully automatic hand-eye calibration. In contrast to previous methods that necessitate manual calibration, specialized markers, or the training of arm-specific neural networks, our approach is the first system that enables accurate calibration of any robot arm in a marker-free, training-free, and fully automatic manner. Our approach employs a two-step process. First, we initialize the camera pose using a sampling or feature-matching-based method with the aid of pretrained image models. Subsequently, we perform pose optimization through differentiable rendering. Extensive experiments demonstrate the system's superior accuracy in both synthetic and real-world datasets across various robot arms and camera settings. Project page: https://ootts.github.io/easyhec_plus.
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