  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.
