Real-Time Grasp Detection Using Improved FMM and Cascaded Neural Networks
Authors: 
Liu Weiwei, Wu Peng, Dong ShiwenAuthors Info & Claims
Liu Weiwei, Wu Peng, Dong ShiwenAuthors Info & ClaimsPages 11 - 15
Abstract
The successful grasping task for the robotic arm requires precise grasping posture. In this paper, we use the cascading depth network to predict the optimal object grasping pose. The model is mainly divided into two steps: i) generating a set of candidates that contain the regions of objects; ii) getting the optimal capture position by detecting the candidate region, and combining the depth image to obtain the three-dimensional coordinates of the capture position for objects. Due to flaws and edge noise in the depth image of Kinect, an improved FMM (Fast Marching Method) algorithm is used to repair the depth image hole, and then the joint bilateral filtering algorithm is employed to recover the edge noise of the depth image. Experimental results in public dataset and real scenes have demonstrated the effectiveness of the proposed method.
References
[1]
I. Lenz, H. Lee, and A. Saxena, "Deep learning for detecting robotic grasps," in Proceedings of Robotics: Science and Systems, Berlin, Germany, June 2013.
[2]
Y. Jiang, S. Moseson, and A. Saxena, "Efficient grasping from rgb-d images: Learning using a new rectangle representation," in IEEE International Conference on Robotics & Automation (ICRA). IEEE, 2011, pp. 3304--3311.
[3]
A. Bicchi and V. Kumar, "Robotic grasping and contact: A review," in IEEE International Conference on Robotics & Automation (ICRA). Citeseer, 2000, pp. 348--353.
[4]
A. T. Miller, S. Knoop, H. I. Christensen, and P. K. Allen, "Automatic grasp planning using shape primitives," in IEEE International Conference on Robotics & Automation(ICRA), vol.2. IEEE, 2003, pp. 1824--1829.
[5]
A. T. Miller and P. K. Allen, "Graspit! a versatile simulator for robotic grasping," Robotics & Automation Magazine, IEEE, vol. 11, no. 4, pp. 110--122, 2004.
[6]
R. Pelossof, A. Miller, P. Allen, and T. Jebara, "An svm learning approach to robotic grasping," in IEEE International Conference on Robotics & Automation (ICRA), vol. 4. IEEE, 2004, pp. 3512--3518.
[7]
B. Léon, S. Ulbrich, R. Diankov, G. Puche, M. Przybylski, A. Morales, T. Asfour, S. Moisio, J. Bohg, J. Kuffner, et al., "Opengrasp: a toolkit for robot grasping simulation," in Simulation, Modeling, and Programming for Autonomous Robots. Springer, 2010, pp. 109--120.
[8]
S. Park and D. Kim, "Study on 3D Action Recognition Based on Deep Neural Network," 2019 International Conference on Electronics, Information, and Communication (ICEIC), Auckland, New Zealand, 2019, pp. 1--3.
[9]
Q. Hou, M. Cheng, X. Hu, A. Borji, Z. Tu and P. H. S. Torr, "Deeply Supervised Salient Object Detection with Short Connections," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 4, pp. 815--828, 1 April 2019.
[10]
X. Chen, K. Kundu, Y. Zhu, H. Ma, S. Fidler and R. Urtasun, "3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1259--1272, 1 May 2018.
[11]
Q. Fu et al., "A Robust RGB-D SLAM System With Points and Lines for Low Texture Indoor Environments," in IEEE Sensors Journal, vol. 19, no. 21, pp. 9908--9920, 1 Nov. 1, 2019.
[12]
S. Yang and S. Scherer, "CubeSLAM: Monocular 3-D Object SLAM," in IEEE Transactions on Robotics, vol. 35, no. 4, pp. 925--938, Aug. 2019.
[13]
A. Telea, "An image inpainting technique based on the fast marching method," in Journal of graphics tools, 2004, 9(1): 23--34.
[14]
J. Kopf, M. F. Cohen, D. Lischinski, et al., "Joint bilateral upsampling," in ACM Transactions on Graphics (ToG), 2007, 26(3): 96--110.
[15]
I. Lenz, H. Lee, and A. Saxena, "Deep learning for detecting robotic grasps," in The International Journal of Robotics Research, 2015, 34(4-5): 705--724.
Index Terms
- Real-Time Grasp Detection Using Improved FMM and Cascaded Neural Networks
Recommendations
An image hole inpainting algorithm with improved FMM for mobile devices
During the warping operation with DIBR, the hole will be generated through the synthesis of 3D image. For mobile device, it has lower performance so that the hole inpainting cannot be completed in real-time. In order to overcome this shortcoming, we ...
A multi-scale descriptor for real time RGB-D hand gesture recognition
Highlights- A new RGB-D shape-based hand gesture recognition method is proposed.
- A new hand ...
AbstractThe development of depth cameras, e.g., the Kinect sensor, provides new opportunities for human computer interaction (HCI). Although the Kinect sensor has been extensively applied for human tracking, human action recognition and hand ...
Estimation of Kinect depth confidence through self-training
All depth data captured by Kinect devices are noisy, and sometimes even lost or shifted, especially around the edges of the depth. In this paper, we propose an approach to generate a per-pixel confidence measurement for each depth map captured by Kinect ...
Comments
Information & Contributors
Information
Published In
June 2020
127 pages
ISBN:9781450377812
DOI:10.1145/3406971
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- University of Macedonia
- NITech: Nagoya Institute of Technology
- Zhejiang University: Zhejiang University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 July 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICGSP 2020
ICGSP 2020: 2020 The 4th International Conference on Graphics and Signal Processing
June 26 - 29, 2020
Nagoya, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 41Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in