Point Pair Feature based 6D pose estimation for robotic grasping

Hejie Fu; Xue Song Mei; Zhaohui Zhang; Wanqiu Zhao; Jun Yang

doi:10.1109/ITNEC48623.2020.9084720

Point Pair Feature based 6D pose estimation for robotic grasping

Hejie Fu
, Xue Song Mei
, Zhaohui Zhang
, Wanqiu Zhao
, Jun Yang

School of Mechanical Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020
Editors	Bing Xu, Kefen Mou
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1803-1808
Number of pages	6
ISBN (Electronic)	9781728143903
DOIs	https://doi.org/10.1109/ITNEC48623.2020.9084720
State	Published - Jun 2020
Event	4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 - Chongqing, China Duration: 12 Jun 2020 → 14 Jun 2020

Publication series

Name	Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

Conference

Conference	4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020
Country/Territory	China
City	Chongqing
Period	12/06/20 → 14/06/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

6D pose estimation
object detection
point pair features
robotic grasping

Access to Document

10.1109/ITNEC48623.2020.9084720

Cite this

Fu, H., Mei, X. S., Zhang, Z., Zhao, W., & Yang, J. (2020). Point Pair Feature based 6D pose estimation for robotic grasping. In B. Xu, & K. Mou (Eds.), Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020 (pp. 1803-1808). Article 9084720 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITNEC48623.2020.9084720

Fu, Hejie ; Mei, Xue Song ; Zhang, Zhaohui et al. / Point Pair Feature based 6D pose estimation for robotic grasping. Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. editor / Bing Xu ; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1803-1808 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020).

@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",

}

Fu, H, Mei, XS, Zhang, Z, Zhao, W & Yang, J 2020, Point Pair Feature based 6D pose estimation for robotic grasping. in B Xu & K Mou (eds), Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020., 9084720, Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1803-1808, 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020, Chongqing, China, 12/06/20. https://doi.org/10.1109/ITNEC48623.2020.9084720

Point Pair Feature based 6D pose estimation for robotic grasping. / Fu, Hejie; Mei, Xue Song; Zhang, Zhaohui et al.
Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. ed. / Bing Xu; Kefen Mou. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1803-1808 9084720 (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Point Pair Feature based 6D pose estimation for robotic grasping

AU - Fu, Hejie

AU - Mei, Xue Song

AU - Zhang, Zhaohui

AU - Zhao, Wanqiu

AU - Yang, Jun

PY - 2020/6

Y1 - 2020/6

N2 - 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.

AB - 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.

KW - 6D pose estimation

KW - object detection

KW - point pair features

KW - robotic grasping

UR - https://www.scopus.com/pages/publications/85086229201

U2 - 10.1109/ITNEC48623.2020.9084720

DO - 10.1109/ITNEC48623.2020.9084720

M3 - 会议稿件

AN - SCOPUS:85086229201

T3 - Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

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BT - Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

A2 - Xu, Bing

A2 - Mou, Kefen

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020

Y2 - 12 June 2020 through 14 June 2020

ER -

Fu H, Mei XS, Zhang Z, Zhao W, Yang J. Point Pair Feature based 6D pose estimation for robotic grasping. In Xu B, Mou K, editors, Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1803-1808. 9084720. (Proceedings of 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2020). doi: 10.1109/ITNEC48623.2020.9084720

Point Pair Feature based 6D pose estimation for robotic grasping

Abstract

Publication series

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UN SDGs

Keywords

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