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Vision-inertia integrated SLAM (Simultaneous Localization and Mapping) method based on genetic algorithm

A technology of genetic algorithm and inertial combination, applied in the SLAM field of visual-inertial combination, can solve the problems of slow update of visual navigation data, influence of camera, accuracy and real-time influence of visual navigation, etc.

Active Publication Date: 2017-05-17
SOUTHEAST UNIV
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Problems solved by technology

But it is susceptible to environmental conditions, such as lighting may affect some cameras
Moreover, the accuracy of image feature point extraction, the speed of matching, and the accuracy and real-time performance of data association will greatly affect visual navigation, and the data update of visual navigation is relatively slow.

Method used

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  • Vision-inertia integrated SLAM (Simultaneous Localization and Mapping) method based on genetic algorithm
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  • Vision-inertia integrated SLAM (Simultaneous Localization and Mapping) method based on genetic algorithm

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Embodiment Construction

[0163] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings;

[0164] Such as Figure 1-5 Shown, a kind of SLAM method based on the visual-inertial combination of genetic algorithm described in the present invention, this method comprises the following steps:

[0165] Step 1: Unify the coordinate system, combine the body coordinate system and navigation coordinate system under inertial navigation with the camera coordinate system and navigation coordinate system under vision. The relationship between the two types of coordinate systems is mainly the rotation matrix R and the translation vector

[0166] Step 2: Calibrate the binocular vision camera, obtain its internal reference matrix, external rotation parameter R and translation parameter t. In this way, the two-dimensional feature point coordinates of the image are corresponding to the three-dimensional motion coordinates of the robot. And through the cali...

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Abstract

The invention discloses a vision-inertia integrated SLAM (Simultaneous Localization and Mapping) method based on a genetic algorithm. The method comprises the following steps: integrating a vision navigation coordinate system with an inertia navigation coordinate system, and calibrating parameters of a binocular camera so as to solve a three-dimensional space coordinate according to an image pixel coordinate; independently calculating by inertia navigation; calculating by vision navigation; integrating vision navigation information with inertia navigation information by using an extended Kalman filter, and building a system filter model; and observing global feature point road signs by using the binocular camera by taking localization locality into account, carrying out data association on map features based on the genetic algorithm, and feeding extended state vectors back to a filter. The method is capable of carrying out long-time and high-accuracy localization; the genetic algorithm is added for improving the data association of the map, so that the simultaneous mapping accuracy is greatly improved.

Description

technical field [0001] The invention relates to the field of navigation, in particular to a SLAM method based on visual-inertial combination of genetic algorithm. Background technique [0002] Robots that move autonomously in unfamiliar environments have become a hot topic in the field of robotics. Unlike traditional robots, autonomous robots do not rely on human control and are not restricted by the environment. They can play a key role in emergency rescue in some dangerous places. role. At the same time, the robot SLAM (simultaneous localization and mapping, real-time positioning and synchronous composition) can not only complete its own accurate positioning, but also extract environmental features to build a map. In an unfamiliar environment, the robot only needs to go through a limited number of walks The entire environment map can be constructed, which helps us to quickly understand the situation of the accident scene in actual situations. Therefore, it is of great th...

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Application Information

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IPC IPC(8): G01C21/00G01C21/16
CPCG01C21/005G01C21/165
Inventor 徐晓苏代维杨博
Owner SOUTHEAST UNIV
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