Robust cylinder detection and pose estimation using 3D point cloud information

This work deals with the problem of detecting cylindrical shapes, commonly found in household and industrial environments, using 3D point cloud information from consumer RBG-D cameras. Existing approaches are fragile in the presence of clutter, in particular flat surfaces, leading to errors during the orientation estimation process that compromise the whole method. We address the aforementioned problem with a novel soft voting scheme that incorporates curvature information in the orientation voting phase. For each potential cylinder point, the principal curvature direction is combined with the normal vector to disambiguate candidate orientations. A set of experiments with synthetically generated data are used to assess the robustness of our method with different levels of clutter and noise. The results demonstrate that incorporating the principal curvature direction within the orientation voting process allows for large improvements on cylinders' parameters estimation. Qualitative results with point clouds acquired from consumer RGB-D cameras, confirm the advantages of the proposed approach.