Efficient 3D object tracking approach based on convolutional neural network and Monte Carlo algorithms used for a pick and place robot

被引:3
|
作者
Zhang, Y.
Zhang, C.
Nestler, R.
Rosenberger, M.
Notni, G.
机构
来源
PHOTONICS AND EDUCATION IN MEASUREMENT SCIENCE | 2019年 / 11144卷
关键词
image processing; 3d object detection; robot vision; deep learning; pick and place robot; 3D tracking; HISTOGRAMS;
D O I
10.1117/12.2530333
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
Currently, Deep Learning (DL) shows us powerful capabilities for image processing. But it cannot output the exact photometric process parameters and shows non-interpretable results. Considering such limitations, this paper presents a robot vision system based on Convolutional Neural Networks (CNN) and Monte Carlo algorithms. As an example to discuss about how to apply DL in industry. In the approach, CNN is used for preprocessing and offline tasks. Then the 6-DoF object position are estimated using a particle filter approach. Experiments will show that our approach is efficient and accurate. In future it could show potential solutions for human-machine collaboration systems.
引用
收藏
页数:6
相关论文
共 50 条
  • [1] Pick and Place of Large Object Based on 3D Vision
    Wu, Hsien-Huang
    Xie, Jia-Kun
    PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS (ICAROB2020), 2020, : 143 - 146
  • [2] A Convolutional Neural Network-Based Method for 3D Object Detection
    Li Y.
    Shi L.
    Wan W.
    Zhao Q.
    Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, 2018, 52 (01): : 7 - 12
  • [3] 3D convolutional neural network for object recognition: a review
    Rahul Dev Singh
    Ajay Mittal
    Rajesh K. Bhatia
    Multimedia Tools and Applications, 2019, 78 : 15951 - 15995
  • [4] A GEOMETRIC CONVOLUTIONAL NEURAL NETWORK FOR 3D OBJECT DETECTION
    Lu, Yawen
    Guo, Qianyu
    Lu, Guoyu
    2019 7TH IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (IEEE GLOBALSIP), 2019,
  • [5] 3D convolutional neural network for object recognition: a review
    Singh, Rahul Dev
    Mittal, Ajay
    Bhatia, Rajesh K.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (12) : 15951 - 15995
  • [6] Multi-Channel Convolutional Neural Network Based 3D Object Detection for Indoor Robot Environmental Perception
    Wang, Li
    Li, Ruifeng
    Shi, Hezi
    Sun, Jingwen
    Zhao, Lijun
    Seah, Hock Soon
    Quah, Chee Kwang
    Tandianus, Budianto
    SENSORS, 2019, 19 (04)
  • [7] Convolutional Neural Network for 3D Object Recognition Based on RGB-D Dataset
    Wang, Jianhua
    Lu, Jinjin
    Chen, Weihai
    Wu, Xingming
    PROCEEDINGS OF THE 2015 10TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, 2015, : 34 - 39
  • [8] An Efficient 3D Model Retrieval Method Based on Convolutional Neural Network
    Ding, Bo
    Tang, Lei
    He, Yong-jun
    COMPLEXITY, 2020, 2020 (2020)
  • [9] Deep Convolutional Neural Network Design Approach for 3D Object Detection for Robotic Grasping
    Sharma, Purvesh
    Valles, Damian
    2020 10TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2020, : 311 - 316
  • [10] A Convolutional Neural Network based 3D Ball Tracking by Detection in Soccer Videos
    Kamble, Paresh R.
    Keskar, Avinash G.
    Bhurchandi, Kishor M.
    ELEVENTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2018), 2019, 11041