scholarly journals Multi-View-Based Pose Estimation and Its Applications on Intelligent Manufacturing

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5072
Author(s):  
Haiwei Yang ◽  
Peilin Jiang ◽  
Fei Wang
Keyword(s):  
Pose Estimation ◽  
Large Scale ◽  
Estimation Method ◽  
Intelligent Manufacturing ◽  
Degree Of Freedom ◽  
Guidance System ◽  
Visual Guidance ◽  
Practical Applications ◽  
Large Size ◽  
Six Degree Of Freedom

Pose estimation is a typical problem in the field of image processing, the purpose of which is to compare or fuse images acquired under different conditions. In recent years, many studies have focused on pose estimation algorithms, but so far there are still many challenges, such as efficiency, complexity and accuracy for various targets and conditions, in the field of algorithm research and practical applications. In this paper, a multi-view-based pose estimation method is proposed. This method can solve the pose estimation problem effectively for large-scale targets and achieve good performance accuracy and stability. Compared with existing methods, this method uses different views (positions and angles), each of which only observes some features of large-size parts, to estimate the six-degree-of-freedom pose of the entire large-size parts. Experimental results demonstrate that the accurate six-degree-of-freedom pose for different targets can be obtained by the proposed method which plays an important role in many actual production lines. What is more, a new visual guidance system, applied into intelligent manufacturing, is presented based on this method. The new visual guidance system has been widely used in automobile manufacturing with high accuracy and efficiency but low cost.

scholarly journals A Multi-Camera Rig with Non-Overlapping Views for Dynamic Six-Degree-of-Freedom Measurement

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 250
Author(s):  
Zhiyuan Niu ◽  
Yongjie Ren ◽  
Linghui Yang ◽  
Jiarui Lin ◽  
Jigui Zhu
Keyword(s):  
Measurement Errors ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Measuring Method ◽  
Intelligent Manufacturing ◽  
Degree Of Freedom ◽  
Feature Correspondence ◽  
Robust Solution ◽  
Vision Measurement ◽  
Six Degree Of Freedom

Large-scale measurement plays an increasingly important role in intelligent manufacturing. However, existing instruments have problems with immersive experiences. In this paper, an immersive positioning and measuring method based on augmented reality is introduced. An inside-out vision measurement approach using a multi-camera rig with non-overlapping views is presented for dynamic six-degree-of-freedom measurement. By using active LED markers, a flexible and robust solution is delivered to deal with complex manufacturing sites. The space resection adjustment principle is addressed and measurement errors are simulated. The improved Nearest Neighbor method is employed for feature correspondence. The proposed tracking method is verified by experiments and results with good performance are obtained.

scholarly journals Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 237 ◽  
Author(s):  
Xuyou Li ◽  
Shitong Du ◽  
Guangchun Li ◽  
Haoyu Li
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Large Scale ◽  
Degree Of Freedom ◽  
Mobile Systems ◽  
Estimation Accuracy ◽  
Scan Matching ◽  
Icp Algorithm ◽  
Localization And Mapping ◽  
Six Degree Of Freedom

Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

scholarly journals Six-degree-of-freedom pose estimation with µm/µrad accuracy based on laser multilateration

2021 ◽  
Vol 10 (1) ◽  
pp. 19-24
Author(s):  
Jan Nitsche ◽  
Matthias Franke ◽  
Nils Haverkamp ◽  
Daniel Heißelmann
Keyword(s):  
Pose Estimation ◽  
Coordinate Measuring Machine ◽  
Degree Of Freedom ◽  
End Effector ◽  
Data Rates ◽  
Measurement Principle ◽  
Measuring Machine ◽  
Position And Orientation ◽  
Six Degree Of Freedom ◽  
Adequate Measurement

Abstract. The estimation of the six-degree-of-freedom position and orientation of an end effector is of high interest in industrial robotics. High precision and data rates are important requirements when choosing an adequate measurement system. In this work, a six-degree-of-freedom pose estimation setup based on laser multilateration is described together with the measurement principle and self-calibration strategies used in this setup. In an experimental setup, data rates of 200 Hz are achieved. During movement, deviations from a reference coordinate measuring machine of 20 µm are observed. During standstill, the deviations are reduced to 5 µm.

Implicitly localized MCMC sampler to cope with nonlocal/nonlinear data constraints in large-size inverse problems

2020 ◽  
Author(s):  
Jean-Michel Brankart
Keyword(s):  
Probability Distribution ◽  
Inverse Problems ◽  
Large Scale ◽  
Prior Probability ◽  
Practical Applications ◽  
Posterior Probability Distribution ◽  
Prior Probability Distribution ◽  
Large Size ◽  
Non Gaussian ◽  
Nonlocal Nonlinear

<p>Many practical applications involve the resolution of large-size inverse problems, without providing more than a moderate-size sample to describe the prior probability distribution. In this situation, additional information must be supplied to augment the effective dimension of the available sample, for instance using a covariance localization approach. In this study, it is suggested that covariance localization can be efficiently applied to an approximate variant of the Metropolis/Hastings algorithm, by modulating the ensemble members by the large-scale patterns of other members. Modulation is used to design a (global) proposal probability distribution (i) that can be sampled at a very low cost, (ii) that automatically accounts for a localized prior covariance, and (iii) that leads to an efficient sampler for the augmented prior probability distribution or for the posterior probability distribution. The resulting algorithm is applied to an academic example, illustrating (i) the effectiveness of covariance localization, (ii) the ability of the method to deal with nonlocal/nonlinear observation operators and non-Gaussian observation errors, (iii) the reliability, resolution and optimality of the updated ensemble, using probabilistic scores appropriate to a non-Gaussian posterior distribution, and (iv) the scalability of the algorithm as a function of the size of the problem. The codes are openly available from github.com/brankart/ensdam.</p>

scholarly journals An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation

Sensors ◽  
2015 ◽  
Vol 15 (7) ◽  
pp. 16448-16465 ◽  
Author(s):  
Changyu He ◽  
Peter Kazanzides ◽  
Hasan Sen ◽  
Sungmin Kim ◽  
Yue Liu
Keyword(s):  
Sensor Fusion ◽  
Pose Estimation ◽  
Optical Sensor ◽  
Degree Of Freedom ◽  
Six Degree Of Freedom ◽  
Fusion Approach

Research on the Dynamic Characteristic of the Bogie in Dynamic Track Stabilizer under the Excitation of the Track Irregularities

2012 ◽  
Vol 178-181 ◽  
pp. 1438-1441
Author(s):  
Li Hua Wang ◽  
Guang Wei Liu ◽  
An Ning Huang ◽  
Ya Yu Huang
Keyword(s):  
Spectral Density ◽  
Dynamic Characteristic ◽  
Time Domain ◽  
Large Scale ◽  
Degree Of Freedom ◽  
Speed Up ◽  
Critical Components ◽  
The Time Domain ◽  
Six Degree Of Freedom ◽  
Track Irregularities

With the large-scale speed-up of the railway, the dynamic track stabilizer will play an important role on the track overhauling and railroading of new line in our country. Bogie is one of the major critical components of the dynamic track stabilizer; its vibrating characteristic will affect the vibrating characteristic of the dynamic track stabilizer directly. The method of numerical simulate was used, based on the spectral density of the track irregularities, the time domain loads of the track irregularities were gained. Then the vibrating characteristics of the dynamic track stabilizer bogie under the excitation of the track irregularities were analyzed on the bases of the ANSYS/LS-DYNA. And the lateral, dilation, ups and downs, nod, swing and anti-rolling vibrating characteristics of the bogie on the six degree of freedom were obtained. The analysis results of this paper will provide foundation for the research on the stationarity and security of the dynamic track stabilizer.

scholarly journals The system design of external cladding installation robot

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096906
Author(s):  
Dong Yang ◽  
Yongbin Li ◽  
Tiejun Li ◽  
Tian Zhang ◽  
Ming Han ◽  
...  
Keyword(s):  
Theoretical Basis ◽  
Large Scale ◽  
Degree Of Freedom ◽  
Test Results ◽  
Robot System ◽  
Automatic Installation ◽  
Advantages And Disadvantages ◽  
Parallel Hybrid ◽  
Six Degree Of Freedom ◽  
Study Designs

In the installation process of large-scale external cladding, the labor intensity is high, and it is dangerous and hard to guarantee good construction quality. In view of these issues, this study designs a construction robot for external cladding installation, as a better solution. First, the advantages and disadvantages of the current building external cladding hanging process are discussed. The components, suitable for automatic installation, are selected to develop an automatic installation process for the building external cladding. According to the process steps and installation action flow, the installation robot is first adjusted. After the design of a step-by-step workflow of positioning and installation, a robot with a six-degree-of-freedom string-mixing mechanism is designed. Finally, a prototype of the proposed external cladding installation robot is developed, and the system is experimentally verified under simulation conditions and real operation. The test results show that the external cladding installation robot system has a strong theoretical basis. The series-parallel hybrid six-degree-of-freedom structure is reasonable, design wise. It helps to complete the automatic installation of the building external cladding and improve the automation level of large-scale plate installations.

scholarly journals ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3489
Author(s):  
Bo Gu ◽  
Jianxun Liu ◽  
Huiyuan Xiong ◽  
Tongtong Li ◽  
Yuelong Pan
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Estimation Method ◽  
Principal Component ◽  
Point Clouds ◽  
Target Point ◽  
Test Environment ◽  
Cloud Data ◽  
Practical Applications ◽  
The Road

In the vehicle pose estimation task based on roadside Lidar in cooperative perception, the measurement distance, angle, and laser resolution directly affect the quality of the target point cloud. For incomplete and sparse point clouds, current methods are either less accurate in correspondences solved by local descriptors or not robust enough due to the reduction of effective boundary points. In response to the above weakness, this paper proposed a registration algorithm Environment Constraint Principal Component-Iterative Closest Point (ECPC-ICP), which integrated road information constraints. The road normal feature was extracted, and the principal component of the vehicle point cloud matrix under the road normal constraint was calculated as the initial pose result. Then, an accurate 6D pose was obtained through point-to-point ICP registration. According to the measurement characteristics of the roadside Lidars, this paper defined the point cloud sparseness description. The existing algorithms were tested on point cloud data with different sparseness. The simulated experimental results showed that the positioning MAE of ECPC-ICP was about 0.5% of the vehicle scale, the orientation MAE was about 0.26°, and the average registration success rate was 95.5%, which demonstrated an improvement in accuracy and robustness compared with current methods. In the real test environment, the positioning MAE was about 2.6% of the vehicle scale, and the average time cost was 53.19 ms, proving the accuracy and effectiveness of ECPC-ICP in practical applications.

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