Monocular line feature map construction method based on epipolar constraint

A map construction and epipolar constraint technology, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of easy degradation of linear features, difficult to determine endpoints, easy to degrade into monocular, etc.

Active Publication Date: 2020-07-28
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

Some scholars have proposed a binocular SLAM algorithm based on line features, but it is not applicable to monocular cameras, and binocular cameras tend to degenerate into monocular for long-distance feature observati...

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  • Monocular line feature map construction method based on epipolar constraint
  • Monocular line feature map construction method based on epipolar constraint
  • Monocular line feature map construction method based on epipolar constraint

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[0068] The accompanying drawings are for illustrative purposes only, and should not be construed as limitations on this patent; in order to better illustrate this embodiment, certain components in the accompanying drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art It is understandable that some well-known structures and descriptions thereof may be omitted in the drawings. The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0069] Such as figure 1 As shown, a monocular feature map construction method based on epipolar constraints, including the following steps:

[0070] S1. For two adjacent frames of image K i ,K j , using the optical flow method to track the harris feature points. If it is the first frame, extract the corner points whose number is N=200. In the subsequent tracking process, if the number ...

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Abstract

The invention provides a monocular line feature map construction method based on epipolar constraint, which comprises the following steps of: 1, tracking feature points of two adjacent frames of images Ki and Kj by using an optical flow tracking method, and searching an essential matrix of a reference angular point and a tracking angular point; 2, extracting LSD line features of each frame of image, and calculating an LBD descriptor; 3, calculating the midpoint epipolar line of each straight line li of the reference frame, and calculating the included angle theta between the midpoint epipolarline and the corresponding matched straight line; 4, selecting points on the straight line from the point set P<i,k> of li, calculating intersection points of the epipolar lines corresponding to the points and the matched straight line, and the intersection point set being I<i,k>; 5, determining a point set P<j,k>; 6, normalizing and triangularizing the point set, determining a space point set Pk,fitting a straight line Lk.S7, re-projecting the straight line to the ith frame and the jth frame, constructing a re-projection error, and updating a camera pose and a space straight line; 8, determining a starting point and an ending point of an endpoint space straight line; 9, updating straight lines.

Description

technical field [0001] The present invention relates to the field of robot positioning and mapping, and more specifically, to a monocular feature map construction method based on epipolar constraints. Background technique [0002] The SLAM (Visual Localization and Mapping) algorithm has broad application prospects in robot positioning and navigation, three-dimensional reconstruction, and automatic driving. In recent years, some scholars have used deep learning training and other methods to estimate depth (Cao, Yuanzhouhan, Zifeng Wu, and Chunhua Shen."Estimating depth from monocular images as classification using deep fully convolutional residual networks."IEEE Transactions on Circuits and Systems for Video Technology [J].2017.), to study the SLAM algorithm based on monocular camera, some scholars have studied the replacement of several modules of the traditional SLAM algorithm with neural networks, such as visual odometry (Li R, Wang S, Long Z, et al. UnDeepVO: Monocular V...

Claims

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Application Information

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IPC IPC(8): G06T7/579G06T7/269G06T7/246
CPCG06T7/579G06T7/269G06T7/248G06T2207/10016Y02T10/40
Inventor 罗永恒魏武周方华李奎霖黄林青
Owner SOUTH CHINA UNIV OF TECH
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