Virtual training and commissioning of industrial bin picking systems using synthetic sensor data and simulation (IMS 2019)

2021 ◽  
pp. 1-10
Author(s):  
Maximilian Metzner ◽  
Felix Albrecht ◽  
Michael Fiegert ◽  
Bastian Bauer ◽  
Susanne Martin ◽  
...  
Keyword(s):  
Sensor Data ◽  
Virtual Training ◽  
Bin Picking ◽  
Picking Systems

scholarly journals Virtual Commissioning of 6 DoF Pose Estimation and Robotic Bin Picking Systems for Industrial Parts

2019 ◽  
Vol 52 (10) ◽  
pp. 160-164
Author(s):  
Maximilian Metzner ◽  
Soeren Weissert ◽  
Engin Karlidag ◽  
Felix Albrecht ◽  
Andreas Blank ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Virtual Commissioning ◽  
Bin Picking ◽  
Picking Systems

Dismounted Infantry Decision Skills Assessment in the Virtual Training Environment

2005 ◽  
Author(s):  
Michael T. Gately ◽  
Sharon M. Watts ◽  
John W. Jaxtheimer ◽  
Robert J. Pleban
Keyword(s):  
Skills Assessment ◽  
Training Environment ◽  
Virtual Training

The Effect of Feedback Specificity in a Virtual Training Environment

2012 ◽  
Author(s):  
Cheryl I. Johnson ◽  
Heather A. Priest-Walker ◽  
Paula J. Durlach ◽  
Stephen R. Serge
Keyword(s):  
Training Environment ◽  
Virtual Training ◽  
Feedback Specificity

Auditory visualization of landmine detector sensor data

2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  
Sensor Data

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

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