Monocular SLAM system initialization algorithm based on dot-line unified framework

Abstract

The invention discloses a monocular SLAM system initialization algorithm based on a dot-line unified framework. The algorithm comprises the following steps: 1, setting an index container with unifieddot-line features, and enabling the obtained dot-line features to be matched and unified in a subsequent random sampling consistency algorithm for calculating F and H matrixes; 2, unifying the line features in a matrix calculation framework, and calculating F and H matrixes and corresponding scores by threads according to the midpoints of the preprocessed line features; and 3, determining a current effective matrix according to the score, and recovering corresponding 3D dot-line features based on the matrix, thereby completing initialization of the monocular SLAM system. The invention providesan initialization method for unifying dot-line characteristics, which ensures that a monocular SLAM system can fully utilize image information, reduces the system initialization difficulty and realizes high-precision initialization.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a monocular SLAM system initialization algorithm based on a point-line unified framework. Background technique [0002] Simultaneous localization and mapping (SLAM) has become more and more important with the development of unmanned driving and drone technology. It refers to the subject equipped with a specific sensor, without the prior information of the environment, to establish a model of the environment during the movement process, and at the same time estimate its own movement. If the camera is used as the sensor, it is called "visual SLAM". [0003] According to the type of sensor, visual SLAM can be divided into three types: monocular camera, binocular camera and depth camera. Due to the inherent "scale uncertainty" problem of monocular cameras, the SLAM system needs to complete the initialization of the system through the first two frames of image information ...

AI Technical Summary

Problems solved by technology

[0004] At present, the initialization method of the indirect method in visual SLAM is mainly to calculate the Fundamental and Homography matrices through the matching feature point pairs, determine the better matrix according to the score, and then use the 4 and 8 sets of rotation matrices R and translation vector t obtained by decomposition, and then Validate the number of valid map points that can be recovered once, and finally determine the only correct R, t matrix. However, it is difficult for this algorithm to obtain effective initialization in low-texture scenes.

[0005] In addition, in recent research, some scholars have proposed a line feature initialization algorit

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