INTEGRATED APPROACH FOR 3D POINT CLOUD SEGMENTATION IN TANK CALIBRATION

The paper presents a hybrid method for segmenting 3D point clouds for the calibration of cylindrical horizontal tanks, combining RANSAC and DBSCAN algorithms with subsequent boundary refinement based on local geometric features. Analysis of prior research indicates that RANSAC is effective for detecting cylindrical surfaces but sensitive to noise, while DBSCAN excels in clustering noisy data but requires parameter optimization. Hybrid methods combining these algorithms demonstrate improved results; however, their robustness to low-density point clouds and accuracy in transition zones remain underexplored. The objective of this study is to develop and evaluate a hybrid 3D point cloud segmentation method integrating RANSAC, DBSCAN, and boundary refinement to achieve automated tank calibration with high accuracy across densities levels ranging from ~1 million to ~18 million points. The research results are based on a comparison of a scanned model (18,012,345 points at maximum density) and an ideal model (17,986,543 points) of the tank. The hybrid method enabled precise estimation of geometric parameters: radius (R ≈ 1.5 m, error ±0.03 m) and length (L ≈ 10.8 m, error ±0.05 m). The segmentation identified the front bottom (372,890 points, ~2.07 %), rear bottom (411,230 points, ~2.28 %), and noise (2,181,240 points, ~12.1 %). The proportionality of point reduction for bottoms with decreasing density was confirmed by linear approximation (Fig. 1): slopes of ~20,700–22,800 points/million for the scanned model and ~20,900–21,100 for the ideal model, with R² ≈ 0.999. Relative segmentation errors range from 0.1–0.7 % for the front bottom and 8.3–8.9 % for the rear bottom, indicating higher accuracy for the front bottom and a need for improvement in the rear bottom. The stability of noise (~12.1–12.2 %) confirms the effectiveness of DBSCAN. The method maintained accuracy even at low density (~1 million points), although the increased error for the rear bottom (~8.75 %) suggests potential loss of detail. In conclusion, the developed hybrid method is robust to noise, scalable for densities levels of 1–18 million points, and suitable for automated tank calibration. The proportionality of components and stable noise level highlight the method’s reliability, while visualization (cylinder – red, front bottom – green, rear bottom – blue) illustrates clear component separation. Future research may focus on optimizing DBSCAN for low-density point clouds and reducing errors for the rear bottom in transition zones.