Abstract
This paper proposes a novel method to refine the 6D pose estimation inferred by an instance-level deep neural network which processes a single RGB image and that has been trained on synthetic images only. The proposed optimization algorithm usefully exploits the depth measurement of a standard RGB-D camera to estimate the dimensions of the considered object, even though the network is trained on a single CAD model of the same object with given dimensions. The improved accuracy in the pose estimation allows a robot to grasp apples of various types and significantly different dimensions successfully; this was not possible using the standard pose estimation algorithm, except for the fruits with dimensions very close to those of the CAD drawing used in the training process. Grasping fresh fruits without damaging each item also demands a suitable grasp force control. A parallel gripper equipped with special force/tactile sensors is thus adopted to achieve safe grasps with the minimum force necessary to lift the fruits without any slippage and any deformation at the same time, with no knowledge of their weight.
Abstract (translated)
本论文提出了一种 novel 的方法，以 refine 由实例级别的深度学习网络处理的一个 RGB 图像并仅训练在合成图像上的实例。该提议的优化算法有效地利用标准 RGB-D 相机的深度测量来估计该对象的尺寸，即使网络训练在一个给定尺寸的同一件对象的 CAD 模型上。姿态估计的准确性提高使得机器人能够成功抓取各种类型和尺寸差异显著的苹果；使用标准姿态估计算法是不可能的，除了尺寸非常接近训练过程中使用的 CAD 绘图的 fruits。抓取新鲜水果而不会损坏每个物品也需要合适的握力控制。因此，采用配备特殊力量/触觉传感器的并行握爪，以使用最小的力量抬起水果，同时避免任何滑动和变形，而不知道它们的重量。
URL
https://arxiv.org/abs/2305.15856
