Visual inertia real-time initialization alignment method and system

Abstract

The invention discloses a visual inertia real-time initialization alignment method and system. The visual inertia real-time initialization alignment method comprises the steps of: acquiring a currentvisual frame, and acquiring a plurality of continuous visual frames of the current visual frame and the measurement data of a corresponding inertia measurement unit; calculating visual frame poses, and performing pre-integration on the measurement data of the inertial measurement unit between two adjacent visual frames to obtain integral data; acquiring an ontology frame pose corresponding to thevisual frame, establishing a linear equation set by utilizing the integral data and the ontology frame pose, and iteratively updating a coefficient matrix and a constant term of the linear equation set; solving the linear equation set to obtain a current ontology frame speed and initial visual frame gravitational acceleration; and if an absolute error between a module value of an initial visual frame gravitational acceleration value and a local gravitational acceleration value is smaller than a preset value, and the value of n is larger than the preset value, judging that visual inertia initial alignment succeeds. The dimension of linear equation set is small, the amount of calculation for solving unknown quantities is smaller, the efficiency is high, and the real-time performance is improved.

Description

Technical field[0001]The present invention relates to the field of information processing technology, in particular to a visual inertial real-time i-alignment method and system.Background technique[0002]In recent years, visual-inertial integrated navigation systems have been increasingly applied to AR, robot navigation, autonomous driving and other fields. Visual-inertial initialization alignment is an essential initialization step for visual-inertial integrated navigation.[0003]The existing visual inertial initialization alignment method usually uses multi-frame visual pose and IMU (Inertial Measurement Unit) pre-integration information to obtain the visual inertial initialization alignment state vector (ontology) by establishing a linear equation set and solving it. The speed of the frame and the gravitational acceleration vector of the initial visual frame, etc.) information. This method integrates multiple frames of visual inertial information to improve accuracy, but the real-t...

AI Technical Summary

Problems solved by technology

This method combines multi-frame visual inertial information, which can improve the accuracy, but the real-time performance is poor. In addition to solving the speed information of the current frame, this method also solves the speed information of all other frames. The speed information of all other frames is very important for vision. For inertial navigation, it is redundant information, which is an unnecessary amount of calculation, so it increases the amount of calculation

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