Tight coupling initialization method for underwater vision inertial navigation pressure positioning

Abstract

The invention belongs to the field of robot positioning, and particularly relates to a tight coupling initialization method for underwater vision inertial navigation pressure positioning. The method comprises the following steps of obtaining the under-scale robot poses and map feature points through a traditional monocular SLAM method, performing pre-integration on the IMU data between the adjacent images to establish a pre-integration residual error between the images; performing pre-integration on the IMU data between the adjacent images and the pressure measurement to establish a pre-integration residual error between the images and the pressure measurement, solving the initialization parameters of a system through a nonlinear optimization method, updating a map by using the initialization parameters, performing light beam adjustment optimization on the updated system, completing an initialization process, and obtaining a more accurate result. Therefore, the operation of a system is promoted. According to the method, the high-frequency IMU information is used for coupling the image information and the pressure information under different time steps, the coupling degree between the initialization parameters is enhanced, and the solving precision of an initialization algorithm is improved.

Description

technical field [0001] The invention belongs to the field of robot positioning, and in particular relates to a tightly coupled initialization method for underwater visual inertial navigation pressure positioning. Background technique [0002] In the positioning of underwater robots, multi-sensor fusion positioning based on graph optimization is a common method, and such methods often require proper initialization to promote the normal operation of the system. For the fusion of visual inertial navigation and pressure, it is necessary to determine the parameters such as the initial scale of the system, IMU bias and initial attitude. Since the time stamps of the camera, IMU and pressure sensor are often not synchronized, and the frequency of the camera and pressure sensor is low, a common method is to pre-integrate the IMU between two frames of images to establish a pre-integration residual factor, and use the pressure sensor information Associated with adjacent image frames t...

AI Technical Summary

Problems solved by technology

Obviously, due to the asynchronous time stamp, the pressure measurement associated with the image information is not the most accurate measurement value at that moment, so this method solves a suboptimal solution, resulting in a large error in the initialization parameters of the final solution, while The error of the initialization parameters will largely affect the subsequent positioning accuracy of the robot

For robot applications that need to rely on robot positioning for subsequent high-precision navigation, this kind of error is not allowed

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