Abstract
This paper presents a novel approach to the automated recognition and localization of 3-D objects. The proposed approach uses 3-D object segmentation to segment randomly stacked objects in an unstructured point cloud. Each segmented object is then represented by a regional area-based descriptor, which measures the distribution of surface area in the oriented bounding box (OBB) of the segmented object. By comparing the estimated descriptor with the template descriptors stored in the database, the object can be recognized. With this approach, the detected object can be matched with the model using the iterative closest point (ICP) algorithm to detect its 3-D location and orientation. Experiments were performed to verify the feasibility and effectiveness of the approach. With the measured point clouds having a spatial resolution of 1.05 mm, the proposed method can achieve both a mean deviation and standard deviation below half of the spatial resolution.
Highlights
Nowadays, both 2-D and 3-D machine vision systems are widely integrated with robot manipulators to enhance the flexibility and versatility of modern manufacturing systems.These intelligent integrated systems can accelerate manufacturing and produce efficiently customized products to enhance competitiveness. 2-D machine vision systems [1,2,3,4] are still used in most integrated systems due to their high accuracy and low cost
The developed 3-D scanner has been integrated with the robotic arm to acquire the 3-D point clouds that represent the randomly stacked objects in the scene
The simulation data provided by Industrial Technology Research Institute (ITRI) comprise the database of six different work parts
Summary
Both 2-D and 3-D machine vision systems are widely integrated with robot manipulators to enhance the flexibility and versatility of modern manufacturing systems.These intelligent integrated systems can accelerate manufacturing and produce efficiently customized products to enhance competitiveness. 2-D machine vision systems [1,2,3,4] are still used in most integrated systems due to their high accuracy and low cost. Both 2-D and 3-D machine vision systems are widely integrated with robot manipulators to enhance the flexibility and versatility of modern manufacturing systems. Systems that involve 3-D data processing can overcome the existing difficulties in 2-D digital imaging by relying on both shape and color information of the objects. These systems have recently been integrated with automated machine manipulators for pick and place applications [5,6,7,8,9,10,11]. Several attempts have been made to solve this nontrivial problem According to their characteristics, the proposed strategies can be divided into graph-based, feature-based, and view-based methods
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