The rapid detection and fine pose estimation of textureless objects in red-green-blue and depth (RGB-D) images are challenging tasks, especially for small dark industrial parts on the production line in clutter scenes. In this paper, a novel practical method based on an RGB-D sensor, which includes 3D object segmentation and 6D pose estimation, is proposed. At the 3D object segmentation stage, 3D virtual and detected bounding boxes are combined to segment 3D scene point clouds. The 3D virtual bounding boxes are determined from prior information on the parts and charging tray, and the 3D detected bounding boxes are obtained from the 2D detected bounding boxes in part detection based on a Single Shot MultiBox Detector (SSD) network in an RGB image. At the 6D pose estimation stage, the coarse pose is estimated by fitting the central axis of the part from the observed 3D point clouds accompanied by a lot of noise, and then refined with part model point clouds by using the iterative closest point (ICP) algorithm. The proposed method has been successfully applied to robotic grasping on the industrial production line with a customer-leverldepth camera. The results verified that grasping speed reaches the subsecond level and that grasping accuracy reaches the millimeter level. The stability and robustness of the automation system meet the production requirement.