Monocular vision and quick-response code road sign based indoor autonomous navigation method

Abstract

The invention discloses a monocular vision and quick-response code road sign based indoor autonomous navigation method. The method includes the steps of S1), acquiring an initial position of a robot, and setting a quick-response route according to the initial position and a target position of the robot; S2, detecting a quick-response code in real time by a monocular camera of the robot, calculating relative position relation among the robot and the quick-response code according to internal and external parameters and determining current navigation information of the robot; S3, performing motion according to the current navigation information of the robot, judging whether a destination address is the target position or not, if yes, completing navigation, or otherwise, performing angle adjustment according to the navigation direction of the quick-response code and turning the step S2). With the method, the problem about complicated calculation of the monocular camera can be solved, and the requirement on accuracy of indoor autonomous navigation can be met.

Description

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AI Technical Summary

Benefits of technology

In this new technology for navigating robots that uses both visual sensors (monocles) and 2D codes, there has been developed an algorithm called Simultaneous Localization And Mapping (SLAM). It allows the robot to accurately locate itself within its environment without relying solely upon cameras or other sources like sonars. By calculating these data with reference points inside the map database, Slamming Navigation System (SLNS), which helps guide them towards their destinations while avoiding obstacles such as walls or furniture, improves efficiency and precision compared to traditional methods.

Problems solved by technology

There exist technical solutions involving different approaches like sensory navigations systems with visual guidance, radioactive ruler markers, lasers, ultrasonic waves, and cameras. These existing ways suffer from various limitations including limited range resolution, difficulty in estimating distances due to factors like soiliness, moisture, temperature variation, and atmospheric turbulence. Additionally, these conventional methods may involve expensive equipment costs and complicated procedures requiring highly skilled operators who lack expert knowledge.

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