Binocular point cloud generation method and system

Abstract

The invention provides a binocular point cloud generation method and system. The method comprises the following steps of: selecting pixel points in an image overlapping area from the acquired image according to a monocular SLAM algorithm; converting the selected pixel points into pixel points corresponding to a binocular SLAM algorithm; calculating the three-dimensional coordinates of the converted pixel points in the image overlapping region based on a binocular SLAM algorithm; and converting the three-dimensional coordinates of the pixel points into coordinate information of a camera coordinate system, calculating the three-dimensional coordinates of the pixel points in the non-overlapping area of the image based on the coordinate information of the camera coordinate system by using themonocular SLAM algorithm, and generating a point cloud according to the three-dimensional coordinates of the pixel points in the non-overlapping area and the overlapping area of the image. According to the scheme of the invention, scale calculation is carried out according to the pixel point information in the image overlapping area of the collected image, and the pixel point information of the non-overlapping area of the image is obtained. The binocular SLAM algorithm is used for assisting the monocular SLAM algorithm to obtain mature points in the image faster and more accurately, and the point cloud generation stability and the object real scale obtaining accuracy are improved.

Description

technical field [0001] The invention relates to the technical field of automobiles, in particular to a binocular point cloud generation method and system. Background technique [0002] At present, in the automatic parking assistance system (Auto Parking Assist, APA) solution, there are generally 4 low-cost fisheye cameras, which are used to realize the function of the Around View Monitoring (AVM) system, or to achieve the same APA-related visual display effects. In the prior art, when a vehicle uses APA to find a parking space, it usually needs to detect parking spaces and static obstacles, such as ice cream cones, wheel chocks, poles and net fences, etc., and even some uncommon obstacles, such as bicycles, chairs and more. Using the currently commonly used machine learning method to detect static obstacles needs to know the type of obstacles and a large amount of training data in advance, which makes the generality and flexibility of finding parking spaces poor. [0003]...

AI Technical Summary

Problems solved by technology

Using the currently commonly used machine learning method to detect static obstacles needs to know the type of obstacle and a large amount of training data in advance. This method makes the versatility and flexibility of finding parking spaces poor.

[0003] In addition, the latest research hotspot at present is to use point cloud to realize static obstacle detection, but usually need to use better global camera (Global Shutter Camera), or binocular camera, and the fisheye camera equipped on a large number of existing cars can'...

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