Extraction of cylinders and estimation of their parameters from point clouds.

In this paper, a novel algorithm is proposed for extracting cylinders and estimating their parameters from 3D point cloud data. First, normal vectors and curvature information are computed for each data point as a preprocessing step. Then potential points that could belong to cylindrical surfaces are extracted by using curvature information. For each potential cylinder point, its neighborhood is considered as inlier points and a robust cylinder fitting algorithm is applied on these inlier points. Inlier points are updated and the fitting process is applied iteratively to propagate to all remaining points belonging to a cylinder. A validation method is proposed to assess whether the detected cylinder is reliable or not. Finally, by applying mean shift clustering, final descriptive parameters of cylinders are estimated accurately. To demonstrate its robustness, the method is tested on both synthetic and complex point clouds with different levels of noise and outliers.