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


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.


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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.