Rigid 3D geometry matching for grasping of known objects in cluttered scenes

Abstract

Abstract-In this paper, we present an efficient 3D object recognition and pose estimation approach for grasping procedures in cluttered and occluded environments. In contrast to common appearance-based approaches, we rely solely on 3D geometry information. Our method is based on a robust geometric descriptor, a hashing technique and an efficient, localized RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method simultaneously recognizes multiple model instances and estimates their pose in the scene. A variety of tests shows that the proposed method performs well on noisy, cluttered and unsegmented range scans in which only small parts of the objects are visible. The main procedure of the algorithm has a linear time complexity resulting in a high recognition speed which allows a direct integration of the method into a continuous manipulation task. The experimental validation with a 7-degrees-of-freedom Cartesian impedance controlled robot shows how the method can be used for grasping objects from a complex random stack. This application demonstrates how the integration of computer vision and softrobotics leads to a robotic system capable of acting in unstructured and occluded environments.

Scene Perception and Recognition in Industrial Environments for Human-Robot Interaction

“…One is an extension of the ORR method [9], [10] method is more suitable for handling arbitrary object geometries and objects having few primitive shapes while the PSG method is more suitable for large models which decompose into a large number of stable primitive shapes. The pipeline for this module is shown in Fig.…”

“…The advantage of these methods, however, lies in their computational speed. Some other methods such as [7], [9], [10] provide robustness to occlusions, partial views and noisy data. However, these methods are rather slow and not suitable for real-time applications in large scenes.…”

“…However, these methods are rather slow and not suitable for real-time applications in large scenes. In this paper, an extension to the Object Recognition RANSAC (ORR) [9], [10] method has been proposed, where the effort has been directed towards a solution which enhances its robustness to noisy sensor data and also increases its speed. Another object recognition and pose estimation algorithm has been proposed, which is complementary to the PSORR method with respect to the target object geometries.…”

Visual perception for the 3D recognition of geometric pieces in robotic manipulation

“…Different methods have been proposed in the literature to manipulate or grasp objects by robotic hands or claws in industrial [1][2][3][4] and/or household environments [5][6][7]. Grasping in industrial environments is mainly focused on resolving pick and place problems.…”

“…To accomplish this goal, they presented a computer vision algorithm combined with a robot control scheme. In household environments, first, Papazov et al [5] proposed a recognition method based solely on 3D geometry information and an efficient localized RANSAC-like sampling strategy.…”