A Novel Pose Estimation Method of Object in Robotic Manipulation Using Vision-Based Tactile Sensor

2021 ◽  
pp. 266-275
Author(s):  
Dan Zhao ◽  
Fuchun Sun ◽  
Quan Zhou ◽  
Zongtao Wang
Keyword(s):  
Pose Estimation ◽  
Estimation Method ◽  
Tactile Sensor ◽  
Robotic Manipulation

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

Non-iterative pose estimation method based on the polarization information via a parallelogram

Measurement ◽  
2022 ◽  
Vol 187 ◽  
pp. 110274
Author(s):  
Zhang Zimiao ◽  
Xu kai ◽  
Wu Yanan ◽  
Zhang Shihai
Keyword(s):  
Pose Estimation ◽  
Estimation Method

Accurate O(n) pose estimation method using both lines and points

Optik ◽  
2016 ◽  
Vol 127 (19) ◽  
pp. 7875-7880
Author(s):  
Meng Li ◽  
Derong Chen ◽  
Jiulu Gong ◽  
Changyuan Wang
Keyword(s):  
Pose Estimation ◽  
Estimation Method

scholarly journals An Accurate and Stable Pose Estimation Method Based on Geometry for Port Hoisting Machinery

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39117-39128
Author(s):  
Qi Wang ◽  
Zhangyan Zhao
Keyword(s):  
Pose Estimation ◽  
Estimation Method

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

scholarly journals End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingrui Luo ◽  
En Li ◽  
Rui Guo ◽  
Jiaxin Liu ◽  
Zize Liang
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Data ◽  
Measurement Unit ◽  
Complex Environments ◽  
End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

Human Pose Estimation Based on Multi-resolution Feature Parallel Network for Public Security

2021 ◽  
Vol 7 (5) ◽  
pp. 1049-1058
Author(s):  
Xiangru Tao ◽  
Cheng Xu ◽  
Hongzhe Liu ◽  
Zhibin Gu
Keyword(s):  
Artificial Intelligence ◽  
Pose Estimation ◽  
Production Management ◽  
Estimation Method ◽  
Smoking Behavior ◽  
Input Image ◽  
Human Pose Estimation ◽  
Human Posture ◽  
Parallel Network ◽  
Human Pose

Smoking detection is an essential part of safety production management. With the wide application of artificial intelligence technology in all kinds of behavior monitoring applications, the technology of real-time monitoring smoking behavior in production areas based on video is essential. In order to carry out smoking detection, it is necessary to analyze the position of key points and posture of the human body in the input image. Due to the diversity of human pose and the complex background in general scene, the accuracy of human pose estimation is not high. To predict accurate human posture information in complex backgrounds, a deep learning network is needed to obtain the feature information of different scales in the input image. The human pose estimation method based on multi-resolution feature parallel network has two parts. The first is to reduce the loss of semantic information by hole convolution and deconvolution in the part of multi-scale feature fusion. The second is to connect different resolution feature maps in the output part to generate the high-quality heat map. To solve the problem of feature loss of previous serial models, more accurate human pose estimation data can be obtained. Experiments show that the accuracy of the proposed method on the coco test set is significantly higher than that of other advanced methods. Accurate human posture estimation results can be better applied to the field of smoking detection, and the smoking behavior can be detected by artificial intelligence, and the alarm will be automatically triggered when the smoking behavior is found.

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