Semi-supervised learning approach for localization and pose estimation of texture-less objects in cluttered scenes

Abstract

3D object recognition and 6D pose estimation are crucial and fundamental endeavours for industrial assembly line automation such as robotic controlled pick-and-place. While the problem on textured objects is extensively studied, it is still an open research topic for texture-less industrial parts, e.g, solid cylinder and hollow tube, which are symmetric and appear similar in shapes from many viewing perspectives, causing pose ambiguity. Also, the industrial assembly line environment is usually cluttered and the captured data is noisy, which makes this task even more challenging. In this paper, we propose a novel object localization and pose estimation technique using RGB images and depth maps of industrial assembly parts. Our segmentation model is fully morphological and unsupervised for localizing the region of interest containing the target object extracted from the depth map. Our segmentation technique is effective in the presence of partial occlusion, multiple objects, and cluttered scenes. We use a model based approach for object recognition based on Stochastic Gradient Descent trained on features of Histogram of Oriented Gradients (HOG) and invariant moments of the region of interest containing the target object. We generate synthetic training images automatically from the CAD models of the industrial parts. We use a contour matching strategy based on Dynamic Time Warping (DTW) algorithm to estimate the optimal 6D pose of the object from a set of candidates. Experimental results show that our proposed approach competes and demonstrates advantages on the challenging T-LESS dataset compared to the state-of-the-art methods. • We introduce a strategy for generating multiple viewpoint synthetic images from a CAD model. • We introduce a non-supervised clustering process for object segmentation based on a depth map that does not require a training stage. • We demonstrate how different HOG feature parameters can be tuned to achieve better prediction and performance. • We present a new real-time strategy for object pose estimation using Dynamic Time Warping (DTW) between the target object contour and best prior candidates. • We show that our method achieves better time performance than other works for pose estimation.