A Geometric Localization Method with Monocular Vision for Robots

2016 ◽  
Author(s):  
Xuesi Li ◽  
Haobin Shi ◽  
Haili Li ◽  
Kao-Shing Hwang ◽  
Huahui Chen
Keyword(s):  
Monocular Vision ◽  
Localization Method ◽  
Geometric Localization

scholarly journals AGV Localization System Based on Ultra-Wideband and Vision Guidance

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 448 ◽  
Author(s):  
Xiaohao Hu ◽  
Zai Luo ◽  
Wensong Jiang
Keyword(s):  
Monocular Vision ◽  
Ultra Wideband ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Localization Method ◽  
Localization System ◽  
Indoor Location ◽  
Accurate Localization ◽  
Vision Localization ◽  
Automated Storage

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.

Differentiating Processes in Object Completion Using a Dot Localization Method

2014 ◽  
Author(s):  
Susan Carrigan ◽  
Evan Palmer ◽  
Philip J. Kellman
Keyword(s):  
Localization Method

Process Induced Defects in the Silicon Substrate: Approaches for Successful Failure Analysis

2010 ◽  
Author(s):  
J.G. van Hassel ◽  
Xiao-Mei Zhang
Keyword(s):  
Failure Analysis ◽  
Case Studies ◽  
Silicon Substrate ◽  
Processing Technique ◽  
Electrical Characteristics ◽  
Localization Method ◽  
The Right ◽  
Induced Defects

Abstract Failures induced in the silicon substrate by process marginalities or process mistakes need continuous attention in new as well as established technologies. Several case studies showing implant related defects and dislocations in silicon will be discussed. Depending on the electrical characteristics of the failure the localization method has to be chosen. The emphasis of the discussion will be on the importance of the right choice for further physical de-processing to reveal the defect. This paper focuses on the localization method, the de- processing technique and the use of Wright etch for subsequent TEM preparation.

Comparison of Active and Passive Voltage Contrast for Failure Localization

2007 ◽  
Author(s):  
R. Rosenkranz ◽  
W. Werner
Keyword(s):  
Case Studies ◽  
Optical Beam ◽  
Leakage Currents ◽  
Localization Method ◽  
Voltage Contrast ◽  
Failure Localization

Abstract In many cases of failure localization, passive voltage contrast (PVC) localization method does not work, because it is not possible to charge up conducting structures which supposed to be dark in the SEM and FIB images. The reason for this is leakage currents. In this article, the authors show how they succeeded in overcoming these difficulties by the application of the active voltage contrast (AVC) method as it was described as biased voltage contrast by Campbell and Soden. They identified three main cases where the PVC didn't work but where they succeeded in failure localization with the AVC method. This is illustrated with the use of two case studies. Compared to the optical beam based methods the resolution is much better so a single failing contact of e.g. 70 nm technology can clearly be identified which cannot be done by TIVA or OBIRCH.

WSN Localization Method Using Interval Data Clustering

2012 ◽  
Vol 38 (7) ◽  
pp. 1190 ◽  
Author(s):  
Yu PENG ◽  
Qing-Hua LUO ◽  
Dan WANG ◽  
Xi-Yuan PENG
Keyword(s):  
Data Clustering ◽  
Interval Data ◽  
Localization Method
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