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

Author(s):  
Juzhong Zhang ◽  
Yuyi Chu ◽  
Zhisen Wang ◽  
Tingfeng Ye ◽  
Liming Cai ◽  
...  
2020 ◽  
Vol 8 (4) ◽  
pp. 296-307
Author(s):  
Konstantin Krestovnikov ◽  
Aleksei Erashov ◽  
Аleksandr Bykov

This paper presents development of pressure sensor array with capacitance-type unit sensors, with scalable number of cells. Different assemblies of unit pressure sensors and their arrays were considered, their characteristics and fabrication methods were investigated. The structure of primary pressure transducer (PPT) array was presented; its operating principle of array was illustrated, calculated reference ratios were derived. The interface circuit, allowing to transform the changes in the primary transducer capacitance into voltage level variations, was proposed. A prototype sensor was implemented; the dependency of output signal power from the applied force was empirically obtained. In the range under 30 N it exhibited a linear pattern. The sensitivity of the array cells to the applied pressure is in the range 134.56..160.35. The measured drift of the output signals from the array cells after 10,000 loading cycles was 1.39%. For developed prototype of the pressure sensor array, based on the experimental data, the average signal-to-noise ratio over the cells was calculated, and equaled 63.47 dB. The proposed prototype was fabricated of easily available materials. It is relatively inexpensive and requires no fine-tuning of each individual cell. Capacitance-type operation type, compared to piezoresistive one, ensures greater stability of the output signal. The scalability and adjustability of cell parameters are achieved with layered sensor structure. The pressure sensor array, presented in this paper, can be utilized in various robotic systems.


2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Cui Li ◽  
Derong Chen ◽  
Jiulu Gong ◽  
Yangyu Wu

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.


2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Francisco Amorós ◽  
Luis Payá ◽  
Oscar Reinoso ◽  
Walterio Mayol-Cuevas ◽  
Andrew Calway

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.


2021 ◽  
Vol 65 (4) ◽  
pp. 583-594
Author(s):  
Anaïs Gauthier ◽  
Mickaël Pruvost ◽  
Olivier Gamache ◽  
Annie Colin

2020 ◽  
Vol 213 ◽  
pp. 107771
Author(s):  
Wilmer Ariza Ramirez ◽  
Zhi Quan Leong ◽  
Hung Duc Nguyen ◽  
Shantha Gamini Jayasinghe

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6940
Author(s):  
Elise Klæbo Vonstad ◽  
Xiaomeng Su ◽  
Beatrix Vereijken ◽  
Kerstin Bach ◽  
Jan Harald Nilsen

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon’s signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.


Sign in / Sign up

Export Citation Format

Share Document