A monocular slam algorithm initialization method and system
An initialization and algorithm technology, applied in the automotive field, can solve problems such as initialization failure, large noise, and large deviation of point cloud generation, and achieve the effect of improving stability, improving calculation accuracy, and good initialization
Active Publication Date: 2021-06-08
ECARX (HUBEI) TECHCO LTD
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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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[0042] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0043] In order to solve the above technical problem, an embodiment of the present invention provides a method for initializing a monocular SLAM algorithm. figure 1 A schematic flow chart of a method for initializing a monocular SLAM algorithm according to an embodiment of the present invention is shown. see figure 1 , the method at least includes step S102 to step S108.
[0044] Step S102 , selecting pixels in an image overlappi...
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The present invention provides a method and system for initializing a monocular SLAM algorithm, the method comprising: selecting pixels in an image overlapping area from a collected image, and converting the selected pixels into pixels corresponding to a binocular SLAM algorithm Finally, based on the binocular SLAM algorithm, the three-dimensional coordinates of the converted pixels in the image overlap area are calculated, and the three-dimensional coordinates of the pixels are converted into the coordinate information of the camera coordinate system, and then the monocular The SLAM algorithm is initialized. Therefore, the embodiment of the present invention can assist the monocular SLAM algorithm to realize initialization according to the binocular SLAM algorithm, so that the monocular system corresponding to the monocular SLAM algorithm can be initialized faster and better, and the monocular SLAM algorithm is improved. stability and calculation accuracy.
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...
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IPC IPC(8): G06T3/00G06T7/70G06T7/73
CPCG06T7/70G06T7/75G06T3/047
Inventor 杨文龙P·尼古拉斯
Owner ECARX (HUBEI) TECHCO LTD