@inproceedings{d094b2704d8e48db8f04bcc07974353d,
title = "Point Pair Feature based 6D pose estimation for robotic grasping",
abstract = "6D pose estimation is a crucial research topic for flexible and autonomous systems. With the development of 3D sensors, methods using range data or point clouds show great potentials. This paper proposes an effective approach to estimate the target object's 6D pose based on point pair features. Several improvements including cluster-based downsampling, neighbor search using K-D tree, multi-frame point clouds fusion and pose verification were made to optimize the performance of the approach. Based on the object's pose, we propose a strategy to grasp the object. We tested our approach in real environment and get 97.5\% success rate of pose estimation and 95.8\% success rate of grasping objects.",
keywords = "6D pose estimation, object detection, point pair features, robotic grasping",
author = "Hejie Fu and Mei, \{Xue Song\} and Zhaohui Zhang and Wanqiu Zhao and Jun Yang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 ; Conference date: 12-06-2020 Through 14-06-2020",
year = "2020",
month = jun,
doi = "10.1109/ITNEC48623.2020.9084720",
language = "英语",
series = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1803--1808",
editor = "Bing Xu and Kefen Mou",
booktitle = "Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020",
}
