Accurate localization method for subaperture stitching interferometry in aspherical optics metrology

2020 ◽  
Vol 91 (7) ◽  
pp. 075114
Author(s):  
Zhuo Zhao ◽  
Bing Li ◽  
Xiaoqin Kang ◽  
Jiasheng Lu ◽  
Xiang Wei ◽  
...  
Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 448 ◽  
Author(s):  
Xiaohao Hu ◽  
Zai Luo ◽  
Wensong Jiang

Aiming at the problems of low localization accuracy and complicated localization methods of the automatic guided vehicle (AGV) in the current automatic storage and transportation process, a combined localization method based on the ultra-wideband (UWB) and the visual guidance is proposed. Both the UWB localization method and the monocular vision localization method are applied to the indoor location of the AGV. According to the corner points of an ArUco code fixed on the AGV body, the monocular vision localization method can solve the pose information of the AGV by the PnP algorithm in real-time. As an auxiliary localization method, the UWB localization method is called to locate the AGV coordinates. The distance from the tag on the AGV body to the surrounding anchors is measured by the time of flight (TOF) ranging algorithm, and the actual coordinates of the AGV are calculated by the trilateral centroid localization algorithm. Then, the localization data of the UWB is corrected by the mean compensation method to obtain a consistent and accurate localization trajectory. The experiment result shows that this localization system has an error of 15mm, which meets the needs of AGV location in the process of automated storage and transportation.


Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4161 ◽  
Author(s):  
Boxin Zhao ◽  
Xiaolong Chen ◽  
Xiaolin Zhao ◽  
Jun Jiang ◽  
Jiahua Wei

Localization in GPS-denied environments has become a bottleneck problem for small unmanned aerial vehicles (UAVs). Smartphones equipped with multi-sensors and multi-core processors provide a choice advantage for small UAVs for their high integration and light weight. However, the built-in phone sensor has low accuracy and the phone storage and computing resources are limited, which make the traditional localization methods unable to be readily converted to smartphone-based ones. The paper aims at exploring the feasibility of the phone sensors, and presenting a real-time, less memory autonomous localization method based on the phone sensors, so that the combination of “small UAV+smartphone” can operate in GPS-denied areas regardless of the overload problem. Indoor and outdoor flight experiments are carried out, respectively, based on an off-the-shelf smartphone and a XAircraft 650 quad-rotor platform. The results show that the precision performance of the phone sensors and real-time accurate localization in indoor environment is possible.


Robotica ◽  
2008 ◽  
Vol 26 (6) ◽  
pp. 817-830 ◽  
Author(s):  
Renato Samperio ◽  
Huosheng Hu ◽  
Francisco Martín ◽  
Vicente Matellán

SUMMARYThis paper describes a hybrid approach to a fast and accurate localization method for legged robots based on Fuzzy-Markov (FM) and Extended Kalman Filters (EKF). Both FM and EKF techniques have been used in robot localization and exhibit different characteristics in terms of processing time, convergence, and accuracy. We propose a Fuzzy-Markov–Kalman (FM–EKF) localization method as a combined solution for a poor predictable platform such as Sony Aibo walking robots. The experimental results show the performance of EKF, FM, and FM-EKF in a localization task with simple movements, combined behaviors, and kidnapped situations. An overhead tracking system was adopted to provide a ground truth to verify the performance of the proposed method.


Measurement ◽  
2019 ◽  
Vol 147 ◽  
pp. 106803
Author(s):  
Chen Shili ◽  
Wu Jialin ◽  
Huang Xinjing ◽  
Li Jian

Author(s):  
W. Wan ◽  
Z. Liu ◽  
K. Di ◽  
B. Wang ◽  
J. Zhou

Localization of the rover is critical to support science and engineering operations in planetary rover missions, such as rover traverse planning and hazard avoidance. It is desirable for planetary rover to have visual localization capability with high degree of automation and quick turnaround time. In this research, we developed a visual localization method for lunar rover, which is capable of deriving accurate localization results from cross-site stereo images. Tie points are searched in correspondent areas predicted by initial localization results and determined by ASIFT matching algorithm. Accurate localization results are derived from bundle adjustment based on an image network constructed by the tie points. In order to investigate the performance of proposed method, theoretical accuracy analysis on is implemented by means of error propagation principles. Field experiments were conducted to verify the effectiveness of the proposed method in practical applications. Experiment results prove that the proposed method provides more accurate localization results (1 %~4 %) than dead-reckoning. After more validations and enhancements, the developed rover localization method has been successfully used in Chang'e-3 mission operations.


2011 ◽  
Vol 339 ◽  
pp. 680-684
Author(s):  
Yin Hua Huang ◽  
Shi Qi Zhao

License plate holds the very small proportion in the whole vehicle image, and the color, size and location for License plate is also uncertain. Fast and accurate localization for license plate is a difficult problem in the license plate recognition process. This paper presents a geometry license plate localization method based on machine vision. This method has very good compatibility to the gradation linear and nonlinear change, moreover support criterion and angle change. Experimental results indicate that the localization deviation does not surpass 1.0pixel and the angular deviation does not surpass 1.0 degrees under same level test condition. Robustness of this localization is basic consistent with Cognex Visionpro. The license plate localization can adapt the low signal-to-noise ratio, the low contrast gradient, the fuzzy situations and so on., which is also simple, practical and is better than traditional zone location and edge localization method.


2020 ◽  
Vol 12 (18) ◽  
pp. 7405
Author(s):  
Dongxue Li ◽  
Kang Yang ◽  
Zhaoyi He ◽  
Hanlin Zhou ◽  
Jiaqi Li

The accurate localization of an acoustic emission (AE) source is a vital aspect of AE nondestructive testing technology. A model of wave velocity attenuation caused by the extension of transmission distance is established to analyze the attenuation of AE wave velocities in concrete and thus improve the acoustic source localization accuracy from the perspective of modified velocity. In combination with the exhaustive and region localization methods, a region exhaustive localization method is established based on the modified wave velocity. The results indicate that the smaller the water–cement ratio, the larger the reference wave velocity, and the spatially dependent attenuation of wave velocity increase. Moreover, the larger the aggregate particle size, the larger the reference wave velocity, and the greater the attenuation of wave velocity with distance. For a propagation distance of 1000 mm, the AE wave velocity attenuation exceeds 50% compared with the AE velocity. The optimized localization method reduces the number of nodes calculated, thus improving the method’s accuracy when used for localization.


2017 ◽  
Vol 25 (02) ◽  
pp. 1750020
Author(s):  
Meijuan Yao ◽  
Licheng Lu ◽  
Li Ma ◽  
Shengming Guo

A new method of target localization based on two broadband guide sources is presented, using which only few environment information need to be known in range-independent shallow water environment. This method, based on warping trasnsform, calculates the replica field by calculating the position information and phase information of the field, respectively. Also, compared with the traditional Matched Field Processing localization method, it can avoid the dependence on environment parameters and the field model. Accurate localization results are obtained when the Signal-Noise ratio is higher than 15[Formula: see text]dB and the range of the target is less than 20[Formula: see text]km.


Sign in / Sign up

Export Citation Format

Share Document