scholarly journals Point Pair Feature-Based Pose Estimation with Multiple Edge Appearance Models (PPF-MEAM) for Robotic Bin Picking

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2719 ◽  
Author(s):  
Diyi Liu ◽  
Shogo Arai ◽  
Jiaqi Miao ◽  
Jun Kinugawa ◽  
Zhao Wang ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Recognition Rate ◽  
Image Data ◽  
Multiple Edge ◽  
Point Pair ◽  
Online Computation ◽  
Bin Picking ◽  
Feature Based ◽  
Appearance Models

Automation of the bin picking task with robots entails the key step of pose estimation, which identifies and locates objects so that the robot can pick and manipulate the object in an accurate and reliable way. This paper proposes a novel point pair feature-based descriptor named Boundary-to-Boundary-using-Tangent-Line (B2B-TL) to estimate the pose of industrial parts including some parts whose point clouds lack key details, for example, the point cloud of the ridges of a part. The proposed descriptor utilizes the 3D point cloud data and 2D image data of the scene simultaneously, and the 2D image data could compensate the missing key details of the point cloud. Based on the descriptor B2B-TL, Multiple Edge Appearance Models (MEAM), a method using multiple models to describe the target object, is proposed to increase the recognition rate and reduce the computation time. A novel pipeline of an online computation process is presented to take advantage of B2B-TL and MEAM. Our algorithm is evaluated against synthetic and real scenes and implemented in a bin picking system. The experimental results show that our method is sufficiently accurate for a robot to grasp industrial parts and is fast enough to be used in a real factory environment.

scholarly journals 3D Object Recognition and Pose Estimation From Point Cloud Using Stably Observed Point Pair Feature

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44335-44345 ◽  
Author(s):  
Deping Li ◽  
Hanyun Wang ◽  
Ning Liu ◽  
Xiaoming Wang ◽  
Jin Xu
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Point Cloud ◽  
3D Object Recognition ◽  
3D Object ◽  
Point Pair

Point Cloud Based Bin Picking: Object Recognition and Pose Estimation Using Region Growing Segmentation Algorithm

2015 ◽  
Vol 791 ◽  
pp. 189-194
Author(s):  
Frantisek Durovsky
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Point Cloud ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Suction Cup ◽  
Bin Picking ◽  
Region Growing Segmentation ◽  
Dual Arm ◽  
Sensor Region

Presented paper describes experimental bin picking using Kinect sensor, region-growing algorithm, latest ROS-Industrial drivers and dual arm manipulator Motoman SDA10f.As well known if manipulation with objects of regular shapes by suction cup is required, it is sufficient to estimate only 5DoF for successful pick. In such a case simpler region growing algorithm may be used instead of complicated 3D object recognition and pose estimation techniques, resulting in shorter processing time and decrease of computational load. Experimental setup for such a scenario, manipulated objects and results using region growing segmentation algorithm are explained in detail. Video and link to open-source code of described application are provided as well.

scholarly journals A Method for 6D Pose Estimation of Free-Form Rigid Objects Using Point Pair Features on Range Data

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2678 ◽  
Author(s):  
Joel Vidal ◽  
Chyi-Yeu Lin ◽  
Xavier Lladó ◽  
Robert Martí
Keyword(s):  
Pose Estimation ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Free Form ◽  
Range Data ◽  
Method Performance ◽  
Point Pair ◽  
Fast Running ◽  
Rigid Objects ◽  
Feature Based

Pose estimation of free-form objects is a crucial task towards flexible and reliable highly complex autonomous systems. Recently, methods based on range and RGB-D data have shown promising results with relatively high recognition rates and fast running times. On this line, this paper presents a feature-based method for 6D pose estimation of rigid objects based on the Point Pair Features voting approach. The presented solution combines a novel preprocessing step, which takes into consideration the discriminative value of surface information, with an improved matching method for Point Pair Features. In addition, an improved clustering step and a novel view-dependent re-scoring process are proposed alongside two scene consistency verification steps. The proposed method performance is evaluated against 15 state-of-the-art solutions on a set of extensive and variate publicly available datasets with real-world scenarios under clutter and occlusion. The presented results show that the proposed method outperforms all tested state-of-the-art methods for all datasets with an overall 6.6% relative improvement compared to the second best method.

scholarly journals A Review: Point Cloud-Based 3D Human Joints Estimation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1684
Author(s):  
Tianxu Xu ◽  
Dong An ◽  
Yuetong Jia ◽  
Yang Yue
Keyword(s):  
Machine Learning ◽  
Pose Estimation ◽  
Human Body ◽  
Point Cloud ◽  
Autonomous Driving ◽  
Joint Estimation ◽  
Research Directions ◽  
Survey Papers ◽  
Feature Based ◽  
Human Joints

Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.

Automatic identification and autonomous sorting of cylindrical parts in cluttered scene based on monocular vision 3D reconstruction

Sensor Review ◽  
2019 ◽  
Vol 39 (6) ◽  
pp. 763-775
Author(s):  
Kun Wei ◽  
Yong Dai ◽  
Bingyin Ren
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Recognition Rate ◽  
Optimal Solution ◽  
Monocular Vision ◽  
Automatic Identification ◽  
Production Lines ◽  
Content Type ◽  
Average Recognition Rate ◽  
Cylindrical Parts

Purpose This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature. Design/methodology/approach The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation. Findings The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s. Practical implications The presented approach can be applied or integrated into automatic sorting production lines in the factories. Originality/value The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.

Sign in / Sign up

Export Citation Format

Share Document