Initialization method and system of monocular SLAM algorithm

Abstract

The invention provides an initialization method and system of a monocular SLAM algorithm. The method comprises the following steps of selecting pixel points in the image overlapping area from the acquired image; converting the selected pixel points into pixel points corresponding to the binocular SLAM algorithm; and calculating the three-dimensional coordinates of the converted pixel points in theimage overlapping area based on a binocular SLAM algorithm, converting the three-dimensional coordinates of the pixel points into coordinate information of a camera coordinate system, and initializing the monocular SLAM algorithm according to the coordinate information of the camera coordinate system. Therefore, according to the embodiment of the invention, the monocular SLAM algorithm can be assisted to realize initialization according to the binocular SLAM algorithm, so that the monocular system corresponding to the monocular SLAM algorithm can realize initialization faster and better, andthe stability and the calculation precision of the monocular SLAM algorithm are improved.

Description

technical field [0001] The invention relates to the technical field of automobiles, in particular to a method and system for initializing a monocular SLAM algorithm. 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...

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't be achieved well

The existing visual SLAM (Simultaneous Localization And Mapping) point cloud generation method based on a monocular fisheye camera mainly has problems such as poor stability, low precision, and difficulty in obtaining the true scale of static obstacles.

Among them, the poor stability is mainly manifested in the frequent initialization failures in the point cloud generation process, the large deviation of point cloud generation, the large noise, and the large scale deviation of the point cloud that can be generated.

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