标志式外骨骼：基于图像的机器人状态估计
Fiducial Exoskeletons: Image-Centric Robot State Estimation
AI总结：
标志式外骨骼通过单图像推断实现机器人状态估计，简化设置并提升校准与控制性能。
摘要：
我们介绍了标志式外骨骼，一种基于图像的3D机器人状态估计重述方法，它用单张图像推断替代了繁琐的流程和以电机为中心的流程。传统方法，尤其是机器人-相机外insic估计，往往依赖高精度执行器，并需要耗时的校准流程，如手眼校准。相比之下，现代基于学习的机器人控制越来越多地从RGB观测在低成本硬件上进行训练和部署。我们的关键见解是双方面的。首先，我们将机器人状态估计视为从单张RGB图像中对每个链接进行6D姿态估计：机器人-相机基变换直接通过估计的基链接姿态获得，而关节状态则通过轻量级的全局优化来恢复，该优化强制使运动学一致性与观测到的链接姿态一致（可选地用编码器读数预热启动）。其次，我们通过引入标志式外骨骼使每个链接的6D姿态估计变得稳健和简单——即使没有学习，也能做到这一点：这是一种轻量级的3D打印支架，每个链接都有一个标志标记，并且已知的标记-链接几何形状。这种设计能够从单张图像中产生稳健的相机-机器人外insic，每个链接的SE(3)姿态，以及关节角度状态，使在断电机器人上也能实现稳健的状态估计。在低成本机器人手臂上进行了演示，标志式外骨骼显著简化了设置，同时提高了校准、状态精度和下游3D控制性能。我们发布了代码和可打印硬件设计，以促进进一步的算法-硬件协同设计。
英文摘要：
We introduce Fiducial Exoskeletons, an image-based reformulation of 3D robot state estimation that replaces cumbersome procedures and motor-centric pipelines with single-image inference. Traditional approaches - especially robot-camera extrinsic estimation - often rely on high-precision actuators and require time-consuming routines such as hand-eye calibration. In contrast, modern learning-based robot control is increasingly trained and deployed from RGB observations on lower-cost hardware. Our key insight is twofold. First, we cast robot state estimation as 6D pose estimation of each link from a single RGB image: the robot-camera base transform is obtained directly as the estimated base-link pose, and the joint state is recovered via a lightweight global optimization that enforces kinematic consistency with the observed link poses (optionally warm-started with encoder readings). Second, we make per-link 6D pose estimation robust and simple - even without learning - by introducing the fiducial exoskeleton: a lightweight 3D-printed mount with a fiducial marker on each link and known marker-link geometry. This design yields robust camera-robot extrinsics, per-link SE(3) poses, and joint-angle state from a single image, enabling robust state estimation even on unplugged robots. Demonstrated on a low-cost robot arm, fiducial exoskeletons substantially simplify setup while improving calibration, state accuracy, and downstream 3D control performance. We release code and printable hardware designs to enable further algorithm-hardware co-design.
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。
