A virtual binocular vision range finding method of remote object based on single rotating angle indexing camera

Di, Hongyu; Shang, Qi; Wang, Sunan

doi:10.1109/icemi.2009.5274442

Cited by 7 publications

(1 citation statement)

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“…He mainly talked about the visual servo system, but did not give a clear result of the experiment. Many researchers used binocular cameras to pose the estimation, which needed to calculate more about the coordinate and transformation matrix [3,4]. Xudong [5] had summarized the importance of the feature extraction for image recognition and computer vision.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot action based on QR code identification

Duan

Chen

et al. 2012

2012 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Nowadays, with the developing of the computer science, computer vision is widely used in many areas. It is still a major problem about feature recognition in complex background and uneven intensity of light environment. Some researchers have already achieved in particular conditions. With the robot stepping into family, it is necessary to conduct in-depth study about feature recognition and access to information in complex indoor environmental conditions. In this paper, we present a fast identification approach for QR code in the indoor environment with normal light intensity. In the process of image feature recognition, it is very difficult for the edge while recognizing the slope image. Therefore, we use vector method to express the color characteristics in the image, and use the square minimum method to determine the edge lines of the pattern. In addition, linear scale transformation makes the image pattern more suitable for standard grids, which can correct the tiny sampling error. Besides, in the experiment of emulation process, we add a colored periphery features to positioning the image. It can be captured clearly due to the special colored background and behave well in the distance of 0.5m to 2m. Using an 860-pixel distance camera, the QR code pattern can be captured easily and identified effectively. A feedback control planar mobile robot is used as a platform to verify the identification method. After the robot heading to the front of the QR code pattern about 0.5m, the data of the sample image can be scanned and read accurately. The experiment results show that the approach is feasible and effective.

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Section: Introductionmentioning

confidence: 99%