Visual SLAM method based on point-line fusion

Abstract

The invention discloses a visual SLAM method based on point-line fusion, and the method comprises the steps: firstly inputting an image, predicting the pose of a camera, extracting a feature point ofthe image, and estimating and extracting a feature line through the time sequence information among a plurality of visual angles; and matching the feature points and the feature lines, tracking the features in front and back frames, establishing inter-frame association, optimizing the pose of the current frame, and optimizing the two-dimensional feature lines to improve the integrity of the feature lines; judging whether the current key frame is a key frame or not, if yes, adding the key frame into the map, updating three-dimensional points and lines in the map, performing joint optimization on the current key frame and the adjacent key frame, and optimizing the pose and three-dimensional characteristics of the camera;and removing a part of external points and redundant key frames; and finally, performing loopback detection on the key frame, if the current key frame and the previous frame are similar scenes, closing loopback, and performing global optimization once to eliminate accumulated errors. Under an SLAM system framework based on points and lines, the line extraction speed and the feature line integrity are improved by utilizing the sequential relationship of multiple view angle images, so that the pose precision and the map reconstruction effect are improved.

Description

technical field [0001] The invention belongs to interdisciplinary disciplines such as image processing, computer graphics and computer vision, and relates to the field of visual simultaneous localization and map construction (SLAM). Background technique [0002] Simultaneous Location and Mapping (SLAM, Simultaneous Location And Mapping for short) is a technology for positioning itself and building a map in an unknown environment. It is mainly used in mobile robots, autonomous driving, virtual reality, augmented reality, etc. Visual SLAM mainly uses visual sensors as input devices, such as monocular cameras, binocular cameras, depth cameras, etc. The system takes images as input and outputs camera trajectories and reconstructed 3D maps. [0003] The mainstream visual SLAM solves the camera trajectory and three-dimensional map by extracting feature points from the image, using the geometric relationship between the feature point and the camera's pose and the three-dimensional...

AI Technical Summary

Problems solved by technology

However, point features do not perform well in scenes such as lighting changes, noise interference, motion blur, and weak textures.

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