GPS and navigation GPS)، GLONASS، GALILEO
MohammadAli Amiri Atashgah; Seyyed Mohammad-Jafar Tabib
Volume 14, Issue 2 , June 2021, , Pages 49-59
Abstract
This research represents localization of an aerial robot using fisheye cameras on walls in a simulation environment. The virtual testbed in this work is a quadrotor that is simulated in MATLAB Simulink. Subsequently, the simulation outputs as flight records are used in a virtual lab, which is developed ...
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This research represents localization of an aerial robot using fisheye cameras on walls in a simulation environment. The virtual testbed in this work is a quadrotor that is simulated in MATLAB Simulink. Subsequently, the simulation outputs as flight records are used in a virtual lab, which is developed in 3DsMAX. Then, the virtual fisheye cameras (here two) are installed in some different points on the walls and the related images from the cameras are received offline. The gathered images will be processed by OpenCV in a C++ environment. For external calibration, each fisheye camera takes an image from a known pattern consist of some lights placed in the virtual lab. We execute Perspective-n-Point method on the images to obtain pierce direction/position of the camera. For more, the aerial robot is localized by computing the nearest point between two lines of sight. In brief, the outcomes exhibit an accuracy of 4cm in the center of the virtual-room room.
Mohammadvali Arbabmir; Masoud Ebrahimi Kachoei
Volume 12, Issue 3 , September 2019, , Pages 43-54
Abstract
In the last decades, the visual navigation system has been investigated by many researchers as an aided navigation system for Inertial Navigation System (INS) in the Unmanned Aerial Vehicles (UAV). In this research, for improving the INS errors a new approach based on feature tracking algorithm is used. ...
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In the last decades, the visual navigation system has been investigated by many researchers as an aided navigation system for Inertial Navigation System (INS) in the Unmanned Aerial Vehicles (UAV). In this research, for improving the INS errors a new approach based on feature tracking algorithm is used. In this approach, in order to estimate the feature points in the current image, the INS states, the feature points of the previous image and dynamic equations are used. Also, in this approach, for improving the estimation of terrain points, the outlier estimated feature points delete. Furthermore, in this article, for improving the altitude error, a barometer is used by the mentioned vision navigation system. The simulation results illustrate the desirable accuracy of the vision system and barometer observations in the update step of Extended Kalman Filter (EKF) and remarkable performance of integrated navigation system for calculating the UAV navigation parameters.
