Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers
Kilian Kleeberger, Jonathan Schnitzler, Muhammad Usman Khalid, Richard Bormann, Werner Kraus, Marco F. Huber
Abstract
This paper introduces a novel approach for the grasping and precise placement of various known rigid objects using multiple grippers within highly cluttered scenes. Using a single depth image of the scene, our method estimates multiple 6D object poses together with an object class, a pose distance for object pose estimation, and a pose distance from a target pose for object placement for each automatically obtained grasp pose with a single forward pass of a neural network.By incorporating model knowledge into the system, our approach has higher success rates for grasping than state-of-the-art model-free approaches. Furthermore, our method chooses grasps that result in significantly more precise object placements than prior model-based work.
BibTeX
@inproceedings{iros2021_preciseobjectpla,
title = {Precise Object Placement with Pose Distance Estimations for Different Objects and Grippers},
author = {Kilian Kleeberger and Jonathan Schnitzler and Muhammad Usman Khalid and Richard Bormann and Werner Kraus and Marco F. Huber},
booktitle = {IROS 2021},
year = {2021}
}