vSLAM implementation method and system based on point and line feature fusion

Abstract

The invention discloses a vSLAM implementation method and system based on point and line feature fusion. The method includes Step S110, obtaining an image frame sequence of a target scene; Step S120,preprocessing each frame of image; Step S130, according to successfully matched point features and lie features, initializing an environmental map; Step S140, tracking based on the environmental map,and estimating the pose of a current frame of image; Step S150, judging whether the current frame of image meets a key frame condition, if yes, executing Step S160, otherwise repeatedly executing StepS110 to Step S150; Step S160, executing a step of a local map thread; Step S170, executing a step of a closed-loop detection thread; and Step S180, executing a step of a global optimization thread toobtain an optimized environmental map and complete simultaneous localization and mapping. The extraction and matching processes of line features are improved, so as to improve the accuracy of data association in the front end, and thus the defects of vSLAM which exist in a complicated low-texture scene can be effectively overcome.

Description

technical field [0001] The present invention relates to the field of visual synchronous localization and map construction (SLAM), in particular to a vSLAM implementation method based on point and line feature fusion and a vSLAM implementation system based on point and line feature fusion. Background technique [0002] Simultaneous localization and mapping (SLAM) originated in the field of robotics, and its goal is to reconstruct the three-dimensional structure of the environment in real time in an unknown environment and simultaneously position the robot itself. Early SFM technology was generally processed offline. Later, with the development of technology, real-time SFM technology appeared, which can be attributed to the scope of SLAM. V-SLAM technology infers the orientation of the camera in the unknown environment based on the captured video information, and constructs the environment map at the same time. Its basic principle is the principle of multi-view geometry. The ...

AI Technical Summary

Problems solved by technology

This method has the following shortcomings: 1. The method of using the Kalman filter to optimize the pose has certain limitations. Since the state variables only retain the pose at the current moment, the pose at the past moment will no longer be updated, resulting in inaccurate estimation The prior information of the method is transmitted to the next moment, and it becomes the cumulative error; 2. This method does not realize the function of closed-loop detection, and has certain expansion limitations

This method has the following deficiencies: 1. The DBoW2 dictionary constructed by the loop detection part only describes the feature points and does not describe the feature lines, and the accuracy of the loop detection is low; 2. The calibration of the binocular camera and the calculation of the pixel depth high complexity

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