A robust and efficient framework for fast cylinder detection

R. Figueiredo, A. Dehban, P. Moreno, A. Bernardino, J. Santos-Victor, H. Araújo

Abstract:

In this work, a complete solution is provided for detecting and identifying cylindrical shapes, which are
commonly found in household and industrial environments, using consumer-grade RGB-D cameras.
Most standard approaches to detect and identify cylinders are not robust to outliers (e.g. points
on other objects in the scene), which limits their applicability in realistic scenes. In addition, these
methods fail to benefit from environmental constraints, e.g. the fact that cylinders often lie or stand
on flat surfaces. To tackle the aforementioned limitations, we introduce three main novelties: (i) a
point cloud soft voting scheme with curvature information that reduces the influence of outliers and
noise, (ii) a selective sampling of the orientation space that favors orientations known a priori, and (iii)
a deep-learning based classifier to filter out objects with non-cylindrical appearance in the 2D images,
thus further improving robustness to outliers.
A set of experiments with synthetically generated data are used to assess the robustness of our
fitting method to different levels of outliers and noise. The results demonstrate that incorporating
the principal curvature direction within the orientation voting process allows for large improvements
on cylinders parameters estimation. Furthermore, we demonstrate that combining the 2D deeplearning cylinder classifier with the 3D orientation voting scheme allows for large speed-up and
accuracy improvements on cylinder identification. The qualitative and quantitative results with real
data acquired from a consumer RGB-D camera, confirm the advantages of the proposed framework.