scholarly journals Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review

2020 ◽  
Author(s):  
Guoguang Du ◽  
Kai Wang ◽  
Shiguo Lian ◽  
Kaiyong Zhao
Keyword(s):  
Pose Estimation ◽  
Object Localization ◽  
Robotic Grasping ◽  
Object Pose Estimation

Object Pose Estimation for Robotic Grasping based on Multi-view Keypoint Detection

2021 ◽  
Author(s):  
Zheyuan Hu ◽  
Renluan Hou ◽  
Jianwei Niu ◽  
Xiaolong Yu ◽  
Tao Ren ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Robotic Grasping ◽  
Keypoint Detection ◽  
Object Pose Estimation

Deep instance segmentation and 6D object pose estimation in cluttered scenes for robotic autonomous grasping

2020 ◽  
Vol 47 (4) ◽  
pp. 593-606
Author(s):  
Yongxiang Wu ◽  
Yili Fu ◽  
Shuguo Wang
Keyword(s):  
Pose Estimation ◽  
Real World ◽  
Synthetic Data ◽  
Robotic Grasping ◽  
Time Efficiency ◽  
Grasp Planning ◽  
Content Type ◽  
Object Pose Estimation ◽  
Robotic Applications ◽  
Instance Segmentation

Purpose This paper aims to design a deep neural network for object instance segmentation and six-dimensional (6D) pose estimation in cluttered scenes and apply the proposed method in real-world robotic autonomous grasping of household objects. Design/methodology/approach A novel deep learning method is proposed for instance segmentation and 6D pose estimation in cluttered scenes. An iterative pose refinement network is integrated with the main network to obtain more robust final pose estimation results for robotic applications. To train the network, a technique is presented to generate abundant annotated synthetic data consisting of RGB-D images and object masks in a fast manner without any hand-labeling. For robotic grasping, the offline grasp planning based on eigengrasp planner is performed and combined with the online object pose estimation. Findings The experiments on the standard pose benchmarking data sets showed that the method achieves better pose estimation and time efficiency performance than state-of-art methods with depth-based ICP refinement. The proposed method is also evaluated on a seven DOFs Kinova Jaco robot with an Intel Realsense RGB-D camera, the grasping results illustrated that the method is accurate and robust enough for real-world robotic applications. Originality/value A novel 6D pose estimation network based on the instance segmentation framework is proposed and a neural work-based iterative pose refinement module is integrated into the method. The proposed method exhibits satisfactory pose estimation and time efficiency for the robotic grasping.

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.

scholarly journals RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1299
Author(s):  
Honglin Yuan ◽  
Tim Hoogenkamp ◽  
Remco C. Veltkamp
Keyword(s):  
Pose Estimation ◽  
Ground Truth ◽  
3D Models ◽  
Depth Image ◽  
Great Success ◽  
Estimation Algorithms ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Image Pairs ◽  
Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

HOT-Net: Non-Autoregressive Transformer for 3D Hand-Object Pose Estimation

2020 ◽  
Author(s):  
Lin Huang ◽  
Jianchao Tan ◽  
Jingjing Meng ◽  
Ji Liu ◽  
Junsong Yuan
Keyword(s):  
Pose Estimation ◽  
Object Pose Estimation

scholarly journals PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning

2017 ◽  
Author(s):  
Alexander Krull ◽  
Eric Brachmann ◽  
Sebastian Nowozin ◽  
Frank Michel ◽  
Jamie Shotton ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Pose Estimation ◽  
Object Pose Estimation
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