论文标题
没有先验对象模型的操作的稀疏密度运动建模和跟踪
Sparse-Dense Motion Modelling and Tracking for Manipulation without Prior Object Models
论文作者
论文摘要
这项工作介绍了一种用于建模和跟踪以前看不见的对象，用于机器人抓握任务。利用对象在场景中的运动，我们的方法将场景从场景中进行了刚性实体，并不断跟踪它们以创建对象和环境的密集且稀疏的模型。虽然密集的跟踪可以与这些模型进行交互，但稀疏跟踪使得这种强大的快速移动，并允许重新绘制已经建模的对象。 对双臂握把任务的评估表明，我们的方法1）使机器人能够在没有先验模型的情况下在线检测新对象，并仅使用简单的参数几何表示，而2）与ART方法相比更为强大。
This work presents an approach for modelling and tracking previously unseen objects for robotic grasping tasks. Using the motion of objects in a scene, our approach segments rigid entities from the scene and continuously tracks them to create a dense and sparse model of the object and the environment. While the dense tracking enables interaction with these models, the sparse tracking makes this robust against fast movements and allows to redetect already modelled objects. The evaluation on a dual-arm grasping task demonstrates that our approach 1) enables a robot to detect new objects online without a prior model and to grasp these objects using only a simple parameterisable geometric representation, and 2) is much more robust compared to the state of the art methods.
