KLT Bin Detection and Pose Estimation in an Industrial Environment

2020 ◽  
pp. 105-118
Author(s):  
Aleksei Beloshapko ◽  
Christian Knoll ◽  
Bilel Boughattas ◽  
Vladimir Korkhov
Keyword(s):  
Pose Estimation ◽  
Industrial Environment

scholarly journals A Novel Metric-Learning-Based Method for Multi-Instance Textureless Objects’ 6D Pose Estimation

2021 ◽  
Vol 11 (22) ◽  
pp. 10531
Author(s):  
Chenrui Wu ◽  
Long Chen ◽  
Shiqing Wu
Keyword(s):  
Pose Estimation ◽  
Real World ◽  
Graph Matching ◽  
Metric Learning ◽  
Intelligent Manufacturing ◽  
Fixed Size ◽  
Industrial Environment ◽  
Real World Applications ◽  
Dense Point ◽  
Detection Stage

6D pose estimation of objects is essential for intelligent manufacturing. Current methods mainly place emphasis on the single object’s pose estimation, which limit its use in real-world applications. In this paper, we propose a multi-instance framework of 6D pose estimation for textureless objects in an industrial environment. We use a two-stage pipeline for this purpose. In the detection stage, EfficientDet is used to detect target instances from the image. In the pose estimation stage, the cropped images are first interpolated into a fixed size, then fed into a pseudo-siamese graph matching network to calculate dense point correspondences. A modified circle loss is defined to measure the differences of positive and negative correspondences. Experiments on the antenna support demonstrate the effectiveness and advantages of our proposed method.

Pose Estimation for 3D Workpiece Grasping in Industrial Environment Based on Evolutionary Algorithm

2012 ◽  
Vol 68 (3-4) ◽  
pp. 293-306 ◽  
Author(s):  
Wei Liu ◽  
Tianshi Chen ◽  
Peng Wang ◽  
Hong Qiao
Keyword(s):  
Evolutionary Algorithm ◽  
Pose Estimation ◽  
Industrial Environment

Model-Based Human Pose Estimation and Its Analysis Using Hausdorff Matching

2015 ◽  
Vol 2 (2) ◽  
pp. 51 ◽  
Author(s):  
Vivek Maik ◽  
Jinho Park ◽  
Daehee Kim ◽  
Joonki Paik
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Model Based ◽  
Its Analysis ◽  
Human Pose

Laser power measurement in industrial environment

2003 ◽  
Author(s):  
Reinhard Kramer ◽  
Harald Schwede ◽  
Klaus Haensel ◽  
Michael Klos
Keyword(s):  
Laser Power ◽  
Power Measurement ◽  
Industrial Environment

3D Object Recognition and Relative Localization using a 3D sensor Embedded on a Mobile Robot

2020 ◽  
Author(s):  
Gopi Krishna Erabati
Keyword(s):  
Pose Estimation ◽  
Autonomous Systems ◽  
Microsoft Kinect ◽  
Object Localization ◽  
Initial Alignment ◽  
Relative Localization ◽  
3D Data ◽  
Industry Automation ◽  
Ofthe Object ◽  
3D Descriptors

The technology in current research scenario is marching towards automation forhigher productivity with accurate and precise product development. Vision andRobotics are domains which work to create autonomous systems and are the keytechnology in quest for mass productivity. The automation in an industry canbe achieved by detecting interactive objects and estimating the pose to manipulatethem. Therefore the object localization ( i.e., pose) includes position andorientation of object, has profound ?significance. The application of object poseestimation varies from industry automation to entertainment industry and fromhealth care to surveillance. The objective of pose estimation of objects is verysigni?cant in many cases, like in order for the robots to manipulate the objects,for accurate rendering of Augmented Reality (AR) among others.This thesis tries to solve the issue of object pose estimation using 3D dataof scene acquired from 3D sensors (e.g. Kinect, Orbec Astra Pro among others).The 3D data has an advantage of independence from object texture and invarianceto illumination. The proposal is divided into two phases : An o?ine phasewhere the 3D model template of the object ( for estimation of pose) is built usingIterative Closest Point (ICP) algorithm. And an online phase where the pose ofthe object is estimated by aligning the scene to the model using ICP, providedwith an initial alignment using 3D descriptors (like Fast Point Feature Transform(FPFH)).The approach we develop is to be integrated on two di?erent platforms :1)Humanoid robot `Pyrene' which has Orbec Astra Pro 3D sensor for data acquisition,and 2)Unmanned Aerial Vehicle (UAV) which has Intel Realsense Euclidon it. The datasets of objects (like electric drill, brick, a small cylinder, cake box)are acquired using Microsoft Kinect, Orbec Astra Pro and Intel RealSense Euclidsensors to test the performance of this technique. The objects which are used totest this approach are the ones which are used by robot. This technique is testedin two scenarios, fi?rstly, when the object is on the table and secondly when theobject is held in hand by a person. The range of objects from the sensor is 0.6to 1.6m. This technique could handle occlusions of the object by hand (when wehold the object), as ICP can work even if partial object is visible in the scene.

Part Detector Based Human Pose Estimation in Images and Videos

2011 ◽  
Vol 33 (6) ◽  
pp. 1413-1419
Author(s):  
Yan-chao Su ◽  
Hai-zhou Ai ◽  
Shi-hong Lao
Keyword(s):  
Pose Estimation ◽  
Human Pose Estimation ◽  
Human Pose
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