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JOIG 2024 Vol.12(2):175-185
doi: 10.18178/joig.12.2.175-185

Improvement of DBSCAN Algorithm Involving Automatic Parameters Estimation and Curvature Analysis in 3D Point Cloud of Piled Pipe

Alfan Rizaldy Pratama 1,2, Bima Sena Bayu Dewantara 3, Dewi Mutiara Sari 3, and Dadet Pramadihanto 3,*
1. Center for Research and Innovation on Advanced Transportation Electrification, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
2. Data Science Department, Faculty of Computer Science, Universitas Pembangunan Nasional Veteran Jawa Timur, Surabaya, Indonesia
3. Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
Email: alfan.rizaldy@upnjatim.ac.id (A.R.P.); bima@pens.ac.id (B.S.B.D.); dewi.mutiara@pens.ac.id (D.M.S.); dadet@pens.ac.id (D.P.)
*Corresponding author

Manuscript received September 15, 2023; revised November 1, 2023; accepted November 17, 2023; published June 14, 2024.

Abstract—Bin-picking in the industrial area is a challenging task since the object is piled in a box. The rapid development of 3D point cloud data in the bin-picking task has not fully addressed the robustness issue of handling objects in every circumstance of piled objects. Density-Based Spatial Clustering of Application with Noise (DBSCAN) as the algorithm that attempts to solve by its density still has a disadvantage like parameter-tuning and ignoring the unique shape of an object. This paper proposes a solution by providing curvature analysis in each point data to represent the shape of an object therefore called Curvature-Density-Based Spatial Clustering of Application with Noise (CVR-DBSCAN). Our improvement uses curvature to analyze object shapes in different placements and automatically estimates parameters like Eps and MinPts. Divided by three algorithms, we call it Auto-DBSCAN, CVR-DBSCAN-Avg, and CVR-DBSCAN-Disc. By using real-scanned Time-of-Flight camera datasets separated by three piled conditions that are well separated, well piled, and arbitrary piled to analyze all possibilities in placing objects. As a result, in well separated, Auto-DBSCAN leads by the stability and accuracy in 99.67% which draws as the DBSCAN using specified parameters. For well piled, CVR-DBSCAN-Avg gives the highest stability although the accuracy can be met with DBSCAN on specified parameters in 98.83%. Last, in arbitrary piled though CVR-DBSCAN-Avg in accuracy lower than DBSCAN which is 73.17% compared to 80.43% the stability is slightly higher with less outlier value. Deal with computational time higher than novel DBSCAN, our improvement made the simplicity and deep analysis in scene understanding.

Keywords—bin-picking, point cloud, density-based clustering, automatic clustering, curvature analysis

Cite: Alfan Rizaldy Pratama, Bima Sena Bayu Dewantara, Dewi Mutiara Sari, and Dadet Pramadihanto, "Improvement of DBSCAN Algorithm Involving Automatic Parameters Estimation and Curvature Analysis in 3D Point Cloud of Piled Pipe," Journal of Image and Graphics, Vol. 12, No. 2, pp. 175-185, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.