4 accepted papers
Object recognition and pose estimation are critical components in autonomous robot manipulation systems, playing a crucial role in enabling robots to interact effectively with the environment. During actual execution, the robot must recognize the object in the current scene, estimate its pose, and t
Conventional methods to autonomous grasping rely on a pre-computed database with known objects to synthesize grasps, which is not possible for novel objects. On the other hand, recently proposed deep learning-based approaches have demonstrated the ability to generalize grasp for unknown objects. How…
Estimating the 6DOF pose of objects is an important function in many applications, such as robot manipulation or augmented reality. However, accurate and fast pose estimation from 3D point clouds is challenging, because of the complexity of object shapes, measurement noise, and presence of occlusion
We present a system capable of reconstructing highly detailed object-level models and estimating the 6D pose of objects by means of an RGB-D camera. In this work, we integrate deeplearning-based semantic segmentation, instance segmentation, and 6D object pose estimation into a state of the art RGB-D