A robust and efficient framework for fast cylinder detection

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 favorsorientations 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 deep-learning 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.