Monocular line feature map construction method based on epipolar constraint

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...

AI Technical Summary

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 observation, and some scholars have proposed a monocular SLAM algorithm based on point-line features , but still cannot solve the problem that the straight line feature is easy to degenerate and the endpoint is not easy to determine

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