Visual inertial navigation fusion SLAM method based on Runge-Kutta4 improved pre-integration

Abstract

The invention belongs to the technical field of unmanned driving, discloses a visual inertial navigation fusion SLAM method based on Runge-Kutta4 improved pre-integration, and is used for solving thetechnical problems of low positioning precision, poor robustness and the like of an existing visual ORB-SLAM2 method in occasions of rapid movement, sparse environmental characteristics and the like.The method comprises the following steps: inputting binocular image pair information; inputting IMU information; preprocessing the binocular image; carrying out pre-integration on the IMU by utilizinga RungeKutta4 algorithm; initializing a system; estimating joint state; locally optimizing a sliding window; and carrying out loop detection and global pose graph optimization. According to the method, positioning estimation and map creation can be effectively carried out in scenes with different difficulty levels, and compared with an original visual ORB-SLAM2 method, the method has higher positioning precision and can be applied to the technical fields of unmanned system navigation, virtual reality and the like.

Description

technical field [0001] The invention belongs to the technical field of unmanned driving, and in particular relates to a visual inertial navigation fusion SLAM method based on Runge-Kutta4 improved pre-integration. Background technique [0002] At present: Unmanned systems such as drones and mobile robots with autonomous navigation functions are gradually being used in various fields. Among many key technologies, Simultaneous Localization And Mapping (SLAM) is the foundation and core of realizing the autonomous navigation function of unmanned systems. Visual SLAM relies on the image sequence captured by the camera to estimate the pose of the unmanned system and create a map of the environment, but when the unmanned system moves too fast or the environment features are missing, its accuracy and robustness will drop sharply, and sometimes even cause localization fail. With continuous advancements in hardware design and fabrication, low-cost lightweight MEMS IMUs have become u...

AI Technical Summary

Problems solved by technology

[0007] (2) Poor positioning accuracy and robustness under extreme conditions such as lack of environmental information and rapid carrier movement

It allows users not to wait too long, but also solves the problem of poor performance of the system in extreme environments such as lack of environmental information, light changes, and fast motion.

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