State estimation from measured data is crucial for robotic applications as autonomous systems rely on sensors to capture the motion and localize in the 3D world. Among sensors that are designed for measuring a robot's pose, or for soft robots, their shape, vision sensors are favorable because they are information-rich, easy to set up, and cost-effective. With recent advancements in computer vision, deep learning-based methods no longer require markers for identifying feature points on the robot. However, learning-based methods are data-hungry and hence not suitable for soft and prototyping robots, as building such bench-marking datasets is usually infeasible. In this work, we achieve image-based robot pose estimation and shape reconstruction from camera images. Our method requires no precise robot meshes, but rather utilizes a differentiable renderer and primitive shapes. It hence can be applied to robots for which CAD models might not be available or are crude. Our parameter estimation pipeline is fully differentiable. The robot shape and pose are estimated iteratively by back-propagating the image loss to update the parameters. We demonstrate that our method of using geometrical shape primitives can achieve high accuracy in shape reconstruction for a soft continuum robot and pose estimation for a robot manipulator.
翻译:由于自主系统依靠传感器来捕捉3D世界的运动和本地化。在为测量机器人的姿势而设计的传感器中,或为软机器人而设计的传感器中,其形状、视觉传感器是有利的,因为它们信息丰富、易于安装和具有成本效益。由于计算机视野最近的进步,深层次的基于学习的方法不再需要识别机器人特征的标记。然而,基于学习的方法是数据饥饿的,因此不适合软机器人和原型机器人,因为建造这种基座标记数据集通常不可行。在这项工作中,我们实现基于图像的机器人以图像的图像为估计和根据相机图像进行重建。我们的方法不需要精确的机器人模版,而是使用一种不同的变形和原始形状。因此,可以适用于那些可能没有CAD模型或模型简陋的机器人。我们的参数估计管道是完全不同的。机器人的形状和形状是通过背对图像丢失进行迭代估计,以便更新图像参数参数。在这项工作中,我们实现基于图像的机器人的估测估测,我们用一个高度的精确度的方法可以实现一个高度的机器人的精确度,从而实现一种高压的机器人的精确度。</s>