Development of a vision based pose estimation system for robotic machining and improving its accuracy using LSTM neural networks and sparse regression

2022 ◽  
Vol 74 ◽  
pp. 102262
Author(s):  
Diyar Khalis Bilal ◽  
Mustafa Unel ◽  
Lutfi Taner Tunc ◽  
Bora Gonul
Keyword(s):  
Neural Networks ◽  
Pose Estimation ◽  
Sparse Regression ◽  
Robotic Machining ◽  
Estimation System

An Automatic Design of Factors in a Human-Pose Estimation System Using Neural Networks

2016 ◽  
Vol 46 (7) ◽  
pp. 875-887 ◽  
Author(s):  
Kai-Chi Chan ◽  
Cheng-Kok Koh ◽  
C. S. George Lee
Keyword(s):  
Neural Networks ◽  
Pose Estimation ◽  
Human Pose Estimation ◽  
Automatic Design ◽  
Human Pose ◽  
Estimation System

Driver pose estimation using convolutional neural networks

2021 ◽  
Vol 29 (4) ◽  
pp. 813-821
Author(s):  
Ren-wen CHEN ◽  
◽  
Ting-ting YUAN ◽  
Wen-bin HUANG ◽  
Yu-xiang ZHANG
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pose Estimation

Vehicle Pose Estimation System Base on Pressure Sensor Array for Clamping Parking Robot

2021 ◽  
Author(s):  
Juzhong Zhang ◽  
Yuyi Chu ◽  
Zhisen Wang ◽  
Tingfeng Ye ◽  
Liming Cai ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Pressure Sensor ◽  
Sensor Array ◽  
Estimation System

scholarly journals FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration

2021 ◽  
Author(s):  
Yu Rong ◽  
Takaaki Shiratori ◽  
Hanbyul Joo
Keyword(s):  
Pose Estimation ◽  
Whole Body ◽  
Estimation System ◽  
Body Pose Estimation

scholarly journals A Closed-Form Solution for Estimating the Accuracy of Circular Feature’ Pose for Object 2D-3D Pose Estimation System

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Cui Li ◽  
Derong Chen ◽  
Jiulu Gong ◽  
Yangyu Wu
Keyword(s):  
Closed Form ◽  
Covariance Matrix ◽  
Pose Estimation ◽  
Closed Form Solution ◽  
Optimization Method ◽  
Form Solution ◽  
Synthetic Image ◽  
Point Selection ◽  
3D Pose Estimation ◽  
Estimation System

Many objects in the real world have circular feature. In general, circular feature’s pose is represented by 5-DoF (degree of freedom) vector ξ = X , Y , Z , α , β T . It is a difficult task to measure the accuracy of circular feature’s pose in each direction and the correlation between each direction. This paper proposes a closed-form solution for estimating the accuracy of pose transformation of circular feature. The covariance matrix of ξ is used to measure the accuracy of the pose. The relationship between the pose of the circular feature of 3D object and the 2D points is analyzed to yield an implicit function, and then Gauss–Newton theorem is employed to compute the partial derivatives of the function with respect to such point, and after that the covariance matrix is computed from both the 2D points and the extraction error. In addition, the method utilizes the covariance matrix of 5-DoF circular feature’s pose variables to optimize the pose estimator. Based on pose covariance, minimize the mean square error (Min-MSE) metric is introduced to guide good 2D imaging point selection, and the total amount of noise introduced into the pose estimator can be reduced. This work provides an accuracy method for object 2D-3D pose estimation using circular feature. At last, the effectiveness of the method for estimating the accuracy is validated based on both random data sets and synthetic images. Various synthetic image sequences are illustrated to show the performance and advantages of the proposed pose optimization method for estimating circular feature’s pose.

scholarly journals Global Appearance Applied to Visual Map Building and Path Estimation Using Multiscale Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Francisco Amorós ◽  
Luis Payá ◽  
Oscar Reinoso ◽  
Walterio Mayol-Cuevas ◽  
Andrew Calway
Keyword(s):  
Pose Estimation ◽  
Visual Information ◽  
Multiscale Analysis ◽  
Spatial Arrangement ◽  
Field Of View ◽  
Indoor Environments ◽  
Map Building ◽  
Topological Map ◽  
Complete Field ◽  
Estimation System

In this work we present a topological map building and localization system for mobile robots based on global appearance of visual information. We include a comparison and analysis of global-appearance techniques applied to wide-angle scenes in retrieval tasks. Next, we define multiscale analysis, which permits improving the association between images and extracting topological distances. Then, a topological map-building algorithm is proposed. At first, the algorithm has information only of some isolated positions of the navigation area in the form of nodes. Each node is composed of a collection of images that covers the complete field of view from a certain position. The algorithm solves the node retrieval and estimates their spatial arrangement. With these aims, it uses the visual information captured along some routes that cover the navigation area. As a result, the algorithm builds a graph that reflects the distribution and adjacency relations between nodes (map). After the map building, we also propose a route path estimation system. This algorithm takes advantage of the multiscale analysis. The accuracy in the pose estimation is not reduced to the nodes locations but also to intermediate positions between them. The algorithms have been tested using two different databases captured in real indoor environments under dynamic conditions.

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