Density-based Clustering for 3D Stacked Pipe Object Recognition using Directly-given Point Cloud Data on Convolutional Neural Network

  • Alfan Rizaldy Pratama Pratama Politeknik Elektronika Negeri Surabaya
  • Bima Sena Bayu Dewantara Politeknik Elektronika Negeri Surabaya
  • Dewi Mutiara Sari Politeknik Elektronika Negeri Surabaya
  • Dadet Pramadihanto Politeknik Elektronika Negeri Surabaya
Keywords: Industrial object, Density-based clustering, 3D object recognition, Convolutional neural network

Abstract

One of the most commonly faced tasks in industrial robots is bin picking.  Much work has been done in this related topic is about grasping and picking an object from the piled bin but ignoring the recognition step in their pipeline. In this paper, a recognition pipeline for industrial bin picking is proposed. Begin with obtaining point cloud data from different manner of stacking objects there are well separated, well piled, and arbitrary piled. Then followed by segmentation using Density-based Spatial Clustering Application with Noise (DBSCAN) to obtain individual object data. The systems then use Convolutional Neural Network (CNN) that consume raw point cloud data. Performance of the segmentation reaches an impressive result in separating objects and network is evaluated under the varying style of stacking objects and give the result with average Accuracy, Recall, Precision, and F1-Score on 98.72%, 95.45%, 99.39%, and 97.33% respectively. Then the obtained model can be used for multiple objects recognition in one scene.

Downloads

Download data is not yet available.

References

Le T-T and Lin C-Y, Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs, Sensors, vol. 19, no. 16, 2019.

Yan W, Xu Z, Zhou X, Su Q, Li S, and Wu H, Fast Object Pose Estimation Using Adaptive Threshold for Bin-Picking, IEEE Access, vol. 8, pp. 63055-63064, 2020.

Sari DM, Pratama AR, Pramadihanto D, and Marta BS, 3D Object Detection Based on Point Cloud Data, Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 7, no. 1, pp. 59-66, 2022.

Kameshwaran K and Malarvizhi K, Survey on Clustering Techniques in Data Mining, (IJCSIT) International Journal of Computer Science and Information Tehcnologies, vol. 5, no. 2, pp. 2272-2276, 2014.

Rusu RB, Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments, Ph.D thesis, Technische Universität München, 2010.

Ester M, Kriegel H-P, Sander J, and Xu X. A, Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226-231, 1996.

Lim GH, Lau N, Pedrosa E, Amaral F, Pereira A and Luís Azevedo J, Precise and efficient pose estimation of stacked objects for mobile manipulation in industrial robotics challenges, Advanced Robotics, vol. 33, no. 13, pp. 636-646, 2019.

Zongming L, Jianxun L, Guodong L and Dong Y, Pose Estimation of Rigid Object in Point Cloud, 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp. 708-713, 2016.

Tombari F, Salti S, and Stefano LD, Unique Signatures of Histograms for Local Surface Description, ECCV'10: Proceedings of the 11th European Conference on Computer Vision, pp. 356-369, 2010.

Rusu RB, Blodow N and Beetz M, Fast Point Feature Histograms (FPFH) for 3D registration, 2009 IEEE International Conference on Robotics and Automation, pp. 3212-3217, 2009.

Rusu RB, Bradski G, Thibaux R and Hsu J, Fast 3D recognition and pose using the Viewpoint Feature Histogram, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2155-2162, 2010.

Wohlkinger W and Vincze M, Ensemble of shape functions for 3D object classification, 2011 IEEE International Conference on Robotics and Biomimetics, pp. 2987-2992, 2011.

Fernandes D, Silva A, Névoa R, Simões C, Gonzalez D and Guevara M, Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy, Information Fusion, vol. 68, pp. 161-191, 2020.

Khaliluzzaman Md, Abu Bakar Siddiq Sayem Md, and Misbah KL, HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition. EMITTER International Journal of Engineering Technology, vol. 9, no. 2, pp. 357-376, 2021.

Bello SA, Yu S, Wang C, Adam JM and Li J, Review: deep learning on 3D point clouds, Remote Sensing, vol. 12, no. 11, pp. 1729, 2020.

Guo Y, Wang H, Hu Q, Liu H, Liu L and Bennamoun M, Deep Learning for 3D Point Clouds: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 12, pp. 4388-4364, 2020.

Qi CR, Su H, Niessner M, Dai A, Yan M and Guibas LJ, Volumetric and Multi-View CNNs for Object Classification on 3D Data, 2016 IEEE Conferene on Computer Vision and Pattern Recognition (CVPR), 2016.

Zhang Y and Rabbat M, A Graph-CNN for 3D Point Cloud Classification, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.

Qi CR, Su H, Mo K and Guibas LJ, PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.

Hanh LD and Duc LM, Planar Object Recognition For Bin Picking Application. 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), pp. 211-215, 2018.

Sun Z, Li Z and Liu Y, An Improved Lidar Data Segmentation Algorithm Based on Euclidean Clustering, Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019), pp. 1119-1130, 2019.

Ahmed SM and Chew CM, Density-Based Clustering for 3D Object Detection in Point Clouds, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10608-10617, 2020.

Czerniawski T, Sankaran B, Nahangi M, Haas C and Leite F, 6D DBSCAN-based segmentation of building point clouds for planar object classification, Automation in Construction, vol. 88, pp. 44-58, 2018.

Campello RJGB, Moulavi D and Sander J, Density-Based Clustering Based on Hierarchical Density Estimates, Advances in Knowledge Discovery and Data Mining, vol. 1819, pp. 160-172, 2013.

Published
2022-06-20
How to Cite
Pratama, A. R. P., Bima Sena Bayu Dewantara, Dewi Mutiara Sari, & Dadet Pramadihanto. (2022). Density-based Clustering for 3D Stacked Pipe Object Recognition using Directly-given Point Cloud Data on Convolutional Neural Network. EMITTER International Journal of Engineering Technology, 10(1), 153-169. https://doi.org/10.24003/emitter.v10i1.704
Section
Articles