EasyHeC++：基于预训练图像模型的完全自动手眼标定
EasyHeC++: Fully Automatic Hand-Eye Calibration with Pretrained Image Models
AI总结：
提出完全自动手眼标定框架EasyHeC++，借助预训练图像模型初始化相机位姿并结合可微渲染优化，无需标记和训练即可高精度标定任意机械臂。
摘要：
手眼标定在机器人学中发挥着基础性作用，直接影响操作和抓取等关键任务的效率。在本研究中，我们提出了一种用于完全自动手眼标定的新框架EasyHeC++。与以往需要手动标定、专用标记或训练特定机械臂神经网络的方法不同，我们的方法是首个能在无标记、免训练且完全自动的方式下，对任意机械臂实现精确标定的系统。该方法采用两步流程：首先，借助预训练图像模型，使用基于采样或特征匹配的方法初始化相机位姿；随后，通过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设置下的合成及真实数据集中均展现出卓越的精度。项目页面：https://ootts.github.io/easyhec_plus.
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
