ORB-SLAM based high-precision vehicle positioning method

A vehicle positioning and high-precision technology, applied in the field of image processing, can solve the problems of inability to obtain location information, failure to save and read maps, etc., and achieve the effects of rich environmental information, low cost, and high robustness

Active Publication Date: 2019-04-16
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

[0007] In the simultaneous positioning and mapping technology SLAM of binocular vision, the algorithm ORB-SLAM, which combines the feature extraction detection and matching algorithm ORB with the simultaneous positioning and mapping technology SLAM, is a relatively mature algorithm, but the algori...

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  • ORB-SLAM based high-precision vehicle positioning method

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Embodiment Construction

[0026] The implementation rate and effect of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0027] refer to figure 1 , the specific implementation steps of this example are as follows:

[0028] Step 1, select the calibration plate and perform calibration.

[0029] Select the calibration plate with the model Pattern2.0, the material is KT plate, 8*6 pattern array, the cell size is 28*28mm, the parallelism error is less than 0.05mm, and the parallelism error between the horizontal line and the reference edge is less than 30 seconds;

[0030] After the calibration board is selected, set the corner points of the chessboard to 8×6, and select the flag position and detection times of the camera calibration function, and set the detection times to 20 times;

[0031] After completing the above preparations, use the OpenCV calibration function to detect 48 image corners, then calculate the calibration error, repeat the...

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Abstract

The invention discloses an ORB-SLAM based high-precision vehicle positioning method, which mainly solves a problem of low accuracy of the positioning result of the current ORB-SLAM classical positioning algorithm. The implementation of the method comprises the steps of selecting a calibration board to calibrate a binocular camera, and carrying out stereo correction on a captured image; detecting ORB feature points in the corrected image, and completing the feature point extraction; matching the extracted feature points by using a binocular sparse feature matching method, then acquiring the current camera pose information by using an adjacent frame feature tracking method, and constructing a local map; carrying out closed-loop detection and global optimization on the constructed local map so as to complete the establishment of a visual map, and preserving the visual map; and selecting a vehicle positioning scheme according to the number of matched feature points in the image, and determining the final position of the target vehicle through reading the visual map. The method disclosed by the invention can improve the vehicle positioning accuracy and robustness, and can be used for artificial intelligence management and safety disposal of unmanned automobiles.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a vehicle positioning method, which can be used for artificial intelligence management and safe disposal of unmanned vehicles. Background technique [0002] Vehicle positioning technology is a key technology in the research of unmanned vehicles. Traditional positioning solutions, such as carrier phase difference technology and magnetic navigation technology, have high deployment costs and limited application range. At the same time, the positioning and mapping technology SLAM calculates its own pose based on sensor information, builds a map at the same time, and obtains high-precision positioning results, which has become a research hotspot in vehicle positioning technology. Simultaneous positioning and mapping technology SLAM can be described as: put a robot into an unknown position in an unknown environment, is there a way for the robot to gradually draw a ...

Claims

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Application Information

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IPC IPC(8): G01C11/30G01C21/32G01S17/02G01S17/88
CPCG01C11/30G01C21/32G01S17/88G01S17/86
Inventor 余航赵乐许录平赵恒旺冯冬竹
Owner XIDIAN UNIV
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