Binocular vision area target depth information extraction and cross section analysis system and method

Abstract

The invention discloses a binocular vision area target depth information extraction and cross section analysis system and method. The system comprises a left bracket, a right bracket, a first driver, a second driver, a third driver, a fourth driver, a fifth driver, a control panel, a first step motor, a second step motor, a third step motor, a fourth step motor, a fifth step motor, a rotating plate, a first transmission gear set, a second transmission gear set, a third transmission gear set, a left rotating shaft, a right rotating shaft, a main shaft electronic compass, a left electronic compass, a right electronic compass, a left camera bracket, a right camera bracket, a left camera, a right camera, a left electronic gyroscope, a right electronic gyroscope, a bracket plate, a bracket plate, a main shaft and a bottom plate, wherein the third transmission gear set consists of a first gear and a second gear; the first transmission gear set consists of a third gear and a fourth gear; the second transmission gear set consists of a fifth gear and a sixth gear. The binocular vision area target depth information extraction and cross section analysis system and method can be used for obtaining the cross section information on preset tracks in areas to be detected.

Description

technical field [0001] The invention belongs to the field of motion control, and relates to a system and method for extracting depth information and profile analysis of a target in a binocular vision area. Background technique [0002] At present, with the development of science and technology and the continuous progress of artificial intelligence, various intelligent vehicles that can walk autonomously have been applied in various fields, such as agricultural picking robots, outer space exploration robots, explosive disposal robots, etc. Their walking The road environment is often complex and unstructured roads. In order to enable the intelligent mobile robot to walk in the complex road environment, it is necessary to obtain the environmental information of the road surface in advance: road surface roughness (protrusions and depressions of the road surface), slope information, etc. Only by fully obtaining the walking information of the road surface can the intelligent mobil...

AI Technical Summary

Problems solved by technology

[0004] However, the contact and non-contact methods in the prior art are mainly used on structured roads, and are not suitable for more complex unstructured roads or field road surface measurements, which cannot enable intelligent mobile robots to obtain real-time road condition information in front of them. , can only be statistically analyzed based on the measured data. Due to the complex and changeable unstructured road walking environment, it is difficult to use the existing data area to analyze the road surface in the area to be passed. Although the laser scanning radar can be used for different road environment detection It is mainly used for obstacle detection on the road surface. It is weak for the analysis of the roughness of the ground and the slope of the road surface, and the equipment is relatively expensive; It is well applied to the unstructured complex road environment, but the current research mainly focuses on obstacle detection and 3D reconstruction of the surrounding environment. 3D reconstruction needs to process a large amount of data, which seriously affects the walking efficiency of intelligent mobile robots and is difficult to meet The real-time requirements of its walking, if the profile information of the walking area of ​​the intelligent mobile robot can be effectively obtained, the walking efficiency of the intelligent mobile robot can be effectively improved. However, the existing technology does not involve how to obtain the area profile information. method

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