Intelligent arm manipulation system in life science labs using H20 mobile robot and Kinect sensor

2016 ◽  
Author(s):  
Mohammed M. Ali ◽  
Hui Liu ◽  
Norbert Stoll ◽  
Kerstin Thurow
Keyword(s):  
Mobile Robot ◽  
Life Science ◽  
Kinect Sensor ◽  
Manipulation System ◽  
Science Labs

scholarly journals Kinect Structural Noise Elimination Technique For ITIS Mobile Robot Data Collector

2018 ◽  
Vol 7 (4.27) ◽  
pp. 1 ◽  
Author(s):  
Aditya Kurniawan ◽  
Kholilatul Wardani
Keyword(s):  
Mobile Robot ◽  
Similarity Index ◽  
Robot Vision ◽  
Structural Similarity ◽  
Positive Influence ◽  
Structural Noise ◽  
Kinect Sensor ◽  
Noise Elimination ◽  
Visual Sensor ◽  
Ir Camera

A contemporary study on the Kinect sensor as a visual sensor device for robots shows that the sensor has some fundamental flaws. One of them are shadows on the object edge that will affect the process of recognition of shape (shape recognition) spatially. If the Kinect sensor is used in the robot vision navigation system, the sensor may lead to errors in the robot's decision on the shape of the object sensed by the sensor. The previous research reports a positive influence on the variation of smoothing process by using neighborhood filtering. This research will use multiple neighborhood localized filtering (MNLF) method to eliminate structural noise generated by kinect sensor IR camera. The robot model that will be used for testing is 6WD Wild Thumper Mobile Robot Chassis from Dagu Robotics. The calculation of SSI (Structural Similarity Index) calculation based on ROI between image index 0 (original image) with index 6 (image result after multiple filtering process) results SSI index with value 0.999999930515914. This indicates that multiple filtering processes do not affect the quality of images produced by Kinect sensors. The number of 0.99 can be rounded to 1 so that the conclusion based on ROI image assessment shows no differences on image quality after process.  

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