The current SLAM methods generally have some drawbacks: such as poor stability and long time-consuming SLAM tasks;in order to solve the problem of poor positioning accuracy of SLAM tasks due to the drawbacks of these ...
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ISBN:
(纸本)9789819743988;9789819743995
The current SLAM methods generally have some drawbacks: such as poor stability and long time-consuming SLAM tasks;in order to solve the problem of poor positioning accuracy of SLAM tasks due to the drawbacks of these SLAM methods, the quality and speed of line feature extraction are improved by improving the traditional line feature extraction method lsd, and the point-line feature fusion with IMU information is fused into the visual inertial SLAM system, which can overcome the difficulties of some previous SLAM systems in facing special environments for SLAM tasks. The experimental validation of this paper's method is carried out by using data from the publicly available dataset EuRoC, and the experimental results show that this paper's visual inertial SLAM method of fusing point and line features has a high positioning accuracy.
In recent years, image processing technology has been developing and maturing, but due to the influence of many interfering factors in the acquisition process, there is a large amount of redundant information in the i...
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In recent years, image processing technology has been developing and maturing, but due to the influence of many interfering factors in the acquisition process, there is a large amount of redundant information in the images obtained. The line segment detection algorithm in image extraction needs to be improved. This study utilizes computer technology to improve the line segment detection technology, and designs a line segment detection algorithm based on the linear detection improvement. Firstly, based on the basic principle of straight line detection algorithm, for the problems of line segment breakage and missing in straight line detection, RGB threechannel grayscale map is applied to detect line segments. Then the detected line segments are connected, merged and deleted. The test results show that the line segment detection algorithm improved based on straight line detection has the highest accuracy rate of 94.50 %, and the average processing time per image is also the lowest at 0.2 s. The algorithm runs faster at 0.25 s and has a higher F -value. It is able to detect the boundaries of a variety of rectangular targets, using the improved line segment detection algorithm has a wide range of applicability, lower error rate, and strong anti-interference ability. The improved line segment detection algorithm has a greater advantage in rectangular target extraction for document, text and book type images.
Remote sensing image registration has been a very important research topic, especially the registration of heterologous images. In the research of the past few years, numerous registration algorithms for heterogenic i...
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Remote sensing image registration has been a very important research topic, especially the registration of heterologous images. In the research of the past few years, numerous registration algorithms for heterogenic images have been developed, especially feature-based matching algorithms, such as point feature-based or line feature-based matching methods. However, there are few matching algorithms that combine line and point features. Therefore, this study proposes a matching algorithm that combines line features and point features while achieving good rotation invariance. It comprises lsd detection of line features, keypoint extraction, and HOG-like feature descriptor construction. The matching performance is compared with state-of-the-art matching algorithms on three heterogeneous image datasets (optical-SAR dataset, optical-infrared dataset, and optical-optical dataset), verifying our method's rotational invariance by rotating images in each dataset. Finally, the experimental results show that our algorithm outperforms the state-of-the-art algorithms in terms of matching performance while possessing very good rotation invariance.
As meter data becomes more and more important in the power industry, detection robots are led into substations for automatic collection of the pointer meters. However, the meter images captured in low illumination env...
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As meter data becomes more and more important in the power industry, detection robots are led into substations for automatic collection of the pointer meters. However, the meter images captured in low illumination environments are unclear, resulting in poor recognition of the meter reading. A low-illumination image enhancement method based on virtual exposure is proposed in this paper, improving the dark and bright areas of low-illumination images, respectively. Then the image fusion was performed based on the Laplace pyramid to obtain clear meter images. In addition, the dial area was extracted using the Hough circle transform, and the pointer's rotation center was fitted using the least squares approach. Finally, the straight line of the pointer was extracted, and the data reading was based on the line segment detector algorithm. Case studies show the above method has good robustness in low illumination environment, with high rate, and accuracy during the image enhancement and automatic reading of the pointer meter.
Owing to the angle deviation of UAV patrol inspection image acquisition, the current detection method of transmission line tower tilt lacks positioning constraints, which leads to the need for repeated judgment of the...
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Owing to the angle deviation of UAV patrol inspection image acquisition, the current detection method of transmission line tower tilt lacks positioning constraints, which leads to the need for repeated judgment of the transmission line tilt. To solve this problem, a method for detecting the inclination of transmission line towers based on the Beidou positioning signal and UAV inspection images is proposed. Constructing a transmission line tower tilt detection framework, the acquisition unit uses the Beidou measurement antenna installed on the tower to obtain its position information and the corresponding tower-monitoring image. After pre-processing by the monitoring unit, the collected information is transmitted to the data center through the communication network. The positioning module uses RTK positioning technology to solve the tower position in real time. The tilt detection module uses the YOLOv4 network to identify the tower target in the monitoring image, and regards it as the area of interest. The lsd algorithm is used to extract the central axis and calculate the tilt angle of the tower profile. Combined with the processing results of the positioning module, the tower tilt angle is monitored. The detection results are presented through the monitoring terminal, and safety warnings are provided. The experimental results show that this method can achieve precise positioning of tower positions, and the positioning results are very close to the actual situation. After 100 training sessions, the network loss can be reduced to 0.06, enabling tower tilt detection and determination of its inclination.
Aiming at the problems of large error in manual reading of pointer meter and limited application environment, this paper proposes a pointer-based instrument recognition method based on image processing. Firstly, the a...
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ISBN:
(纸本)9781450387811
Aiming at the problems of large error in manual reading of pointer meter and limited application environment, this paper proposes a pointer-based instrument recognition method based on image processing. Firstly, the acquired instrument image is preprocessed, and the pointer area is extracted by using image processing methods such as edge detection and contour tracking; Secondly, the lsd algorithm is used to extract the straight line segments of the graduated scale, and the center and radius of the dial are determined by fitting the circle at the midpoints of these straight line segments; Thirdly, the central tick mark is used to extract the main tick marks in the tick loop band, and the accuracy of the main tick mark extraction is improved by checking the relationship of the angle difference between adjacent tick marks; Then, the improved Hough transform method is used to detect the pointer line, the KNN algorithm performs character recognition, and the adjacent characters satisfying the preset distance and angle relationship are combined and stored to complete the matching of the main tick marks and values; Finally, a distance method is used to automatically read the pointer meter. The experimental results show that the method can get the meter reading better under different illumination and background conditions, and the error is smaller than the manual reading. The algorithm is stable and reliable.
PNP (Perspective-N-Point) and PNL (Perspective-N-Line) are the most commonly used methods for camera pose estimation. However, most methods are based on the hypothesis of the pinhole camera model, which ignore camera ...
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ISBN:
(纸本)9781538681787
PNP (Perspective-N-Point) and PNL (Perspective-N-Line) are the most commonly used methods for camera pose estimation. However, most methods are based on the hypothesis of the pinhole camera model, which ignore camera distortion's effects. Considering the influences of camera distortion, we proposed to solve the camera pose directly by using distorted lines. In this paper, we first undistort the lines according to distortion model, then use lsd algorithm extract 2D undistorted lines from the image. Having matched 2D/3D lines, the PNL (Perspective-N-Line) was used finally to solve the camera pose. Through experiments on synthetic images and real images, it turns out that our method is effective and feasible for pose estimation.
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