Enhanced point cloud registration for workpieces using triangular constraint sampling consistency in complex industrial environment

TY - JOUR

T1 - Enhanced point cloud registration for workpieces using triangular constraint sampling consistency in complex industrial environment

AU - Guo, Zhentao

AU - Ma, Hongbin

N1 - Publisher Copyright: © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026.

PY - 2026/3

Y1 - 2026/3

N2 - Accurately identifying and locating workpieces in complex industrial environment poses significant challenges due to high noise levels and large point cloud datasets. This paper proposes a method for acquiring workpiece point clouds using 3D structured light measurement for generating high-quality and reliable workpiece datasets. The proposed approach combines noise filtering, adaptive downsampling, PCA-FPFH feature extraction, and TCSAC (triangular constraint sampling consistency) to address the challenges posed by excessive noise and the substantial size of point cloud datasets, which significantly increase the computational overhead of registration algorithms. Additionally, the proposed method addresses the issue where most workpieces in industrial environment have similar features, which reduces the matching accuracy of point cloud registration. Experimental results demonstrate that our method improves both the accuracy and speed of point cloud registration in complex industrial environment and demonstrates strong performance on the 3DMatch, 3DLoMatch, and odometryKITTI open-source benchmarks. The relevant code is available at https://github.com/ZhentaoGuo/TCSAC.

AB - Accurately identifying and locating workpieces in complex industrial environment poses significant challenges due to high noise levels and large point cloud datasets. This paper proposes a method for acquiring workpiece point clouds using 3D structured light measurement for generating high-quality and reliable workpiece datasets. The proposed approach combines noise filtering, adaptive downsampling, PCA-FPFH feature extraction, and TCSAC (triangular constraint sampling consistency) to address the challenges posed by excessive noise and the substantial size of point cloud datasets, which significantly increase the computational overhead of registration algorithms. Additionally, the proposed method addresses the issue where most workpieces in industrial environment have similar features, which reduces the matching accuracy of point cloud registration. Experimental results demonstrate that our method improves both the accuracy and speed of point cloud registration in complex industrial environment and demonstrates strong performance on the 3DMatch, 3DLoMatch, and odometryKITTI open-source benchmarks. The relevant code is available at https://github.com/ZhentaoGuo/TCSAC.

KW - Adaptive downsampling

KW - Industrial environment

KW - PCA-FPFH feature extraction

KW - Point cloud registration

KW - TCSAC

UR - https://www.scopus.com/pages/publications/105035008485

U2 - 10.1007/s00371-026-04421-1

DO - 10.1007/s00371-026-04421-1

M3 - Article

AN - SCOPUS:105035008485

SN - 0178-2789

VL - 42

JO - Visual Computer

JF - Visual Computer

IS - 5

M1 - 221

ER -