Calibration of computer vision positioning system for MEMS wire bonder

2007 ◽  
Author(s):  
Junlan Li ◽  
Yizhong Wang ◽  
Xingyu Zhao ◽  
Fanzhi Kong ◽  
Dawei Zhang
Keyword(s):  
Computer Vision ◽  
Positioning System ◽  
Vision Positioning

scholarly journals An autonomous navigational system using GPS and computer vision for futuristic road traffic

2022 ◽  
Vol 12 (1) ◽  
pp. 179
Author(s):  
Prabha Ramasamy ◽  
Mohan Kabadi
Keyword(s):  
Computer Vision ◽  
Global Positioning System ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Road Traffic ◽  
Positioning System ◽  
Traffic System ◽  
Global Positioning ◽  
Navigational System

Navigational service is one of the most essential dependency towards any transport system and at present, there are various revolutionary approaches that has contributed towards its improvement. This paper has reviewed the global positioning system (GPS) and computer vision based navigational system and found that there is a large gap between the actual demand of navigation and what currently exists. Therefore, the proposed study discusses about a novel framework of an autonomous navigation system that uses GPS as well as computer vision considering the case study of futuristic road traffic system. An analytical model is built up where the geo-referenced data from GPS is integrated with the signals captured from the visual sensors are considered to implement this concept. The simulated outcome of the study shows that proposed study offers enhanced accuracy as well as faster processing in contrast to existing approaches.

Development of Machine Vision Positioning System of Magnetorheological Fluid (MRF) Actuator

2013 ◽  
Vol 470 ◽  
pp. 625-629
Author(s):  
A.B. Husaini ◽  
Ghazali Izzat ◽  
Samad Zahurin ◽  
Othman Rusli
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Feedback System ◽  
Magnetorheological Fluid ◽  
Fast Response ◽  
Positioning System ◽  
Machine Vision System ◽  
Vision Positioning ◽  
Magnetorheological Valve

Magnetorheological valve offers several advantages such as controllability, small in size and no moving part during operation. Thus, many researchers are working on developing an actuator based on this valve. However, this actuator required feedback system to improve it precision. This research is focusing on developing of machine vision based positioning system for MRF actuator. Image processing algorithms coded using Matlab software and directly connect to MRF valve controller. As a result, the system shows a fast response with processing time only 0.6 millisecond, system resolution is 0.1 millimeter and finally repeatability is 0.01. As a conclusion, the machine vision system are applicable for MRF actuator positioning system. This study is significant in order to developing a low cost and robust positioning system.

scholarly journals Indoor Localization of Quadcopters in Industrial Environment

2020 ◽  
Author(s):  
Péter Troll ◽  
Károly Szipka ◽  
Andreas Archenti
Keyword(s):  
Computer Vision ◽  
Material Handling ◽  
Evaluation Method ◽  
Indoor Localization ◽  
Research Work ◽  
Fine Tuning ◽  
Measurement Unit ◽  
Positioning System ◽  
Estimation System ◽  
Slam Algorithm

The research work in this paper was carried out to reach advanced positioning capabilities of unmanned aerial vehicles (UAVs) for indoor applications. The paper includes the design of a quadcopter and the implementation of a control system with the capability to position the quadcopter indoor using onboard visual pose estimation system, without the help of GPS. The project also covered the design and implementation of quadcopter hardware and the control software. The developed hardware enables the quadcopter to raise at least 0.5kg additional payload. The system was developed on a Raspberry single-board computer in combination with a PixHawk flight controller. OpenCV library was used to implement the necessary computer vision. The Open-source software-based solution was developed in the Robotic Operating System (ROS) environment, which performs sensor reading and communication with the flight controller while recording data about its operation and transmits those to the user interface. For the vision-based position estimation, pre-positioned printed markers were used. The markers were generated by ArUco coding, which exactly defines the current position and orientation of the quadcopter, with the help of computer vision. The resulting data was processed in the ROS environment. LiDAR with Hector SLAM algorithm was used to map the objects around the quadcopter. The project also deals with the necessary camera calibration. The fusion of signals from the camera and from the IMU (Inertial Measurement Unit) was achieved by using Extended Kalman Filter (EKF). The evaluation of the completed positioning system was performed with an OptiTrack optical-based external multi-camera measurement system. The introduced evaluation method has enough precision to be used to investigate the enhancement of positioning performance of quadcopters, as well as fine-tuning the parameters of the used controller and filtering approach. The payload capacity allows autonomous material handling indoors. Based on the experiments, the system has an accurate positioning system to be suitable for industrial application.

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