GISR：基于几何初始化与轮廓细化的单视角机器人位姿与构型估计
GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation
AI总结：
本文提出GISR方法，通过几何初始化与深度迭代轮廓细化两个模块，从单视角实时估计机器人手臂的位姿与构型，在速度和精度上优于同类方法。
摘要：
在自主机器人领域，测量机器人内部状态并感知其环境（包括与协作机器人等其他智能体的交互）至关重要。从单视角估计机器人手臂的位姿有望替代传统的眼到手标定方法，并且对于在线估计和动态环境尤其具有吸引力。除位姿外，恢复机器人构型还能提供对所观测机器人的完整空间理解，可在高级机器人应用场景中用于预测其他智能体的动作。此外，这种额外的冗余性使得在传感器故障或外部干扰情况下能够规划并执行恢复协议。我们提出了GISR——一种优先保证实时执行的深度构型与机器人到相机位姿估计方法。GISR由两个模块组成：（i）一个几何初始化模块，高效计算近似的机器人位姿和构型；以及（ii）一个基于深度迭代轮廓的细化模块，仅需几次迭代即可得到最终解。我们在公开数据上评估了GISR，结果表明它在速度和精度两方面均优于同类现有方法，并且能够与依赖真值本体感知且仅恢复位姿的方法相竞争。
英文摘要：
In autonomous robotics, measurement of the robot's internal state and perception of its environment, including interaction with other agents such as collaborative robots, are essential. Estimating the pose of the robot arm from a single view has the potential to replace classical eye-to-hand calibration approaches and is particularly attractive for online estimation and dynamic environments. In addition to its pose, recovering the robot configuration provides a complete spatial understanding of the observed robot that can be used to anticipate the actions of other agents in advanced robotics use cases. Furthermore, this additional redundancy enables the planning and execution of recovery protocols in case of sensor failures or external disturbances. We introduce GISR - a deep configuration and robot-to-camera pose estimation method that prioritizes execution in real-time. GISR consists of two modules: (i) a geometric initialization module that efficiently computes an approximate robot pose and configuration, and (ii) a deep iterative silhouette-based refinement module that arrives at a final solution in just a few iterations. We evaluate GISR on publicly available data and show that it outperforms existing methods of the same class in terms of both speed and accuracy, and can compete with approaches that rely on ground-truth proprioception and recover only the pose.
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。
