Multi-sensor data fusion method and device, electronic equipment and medium

Abstract

The embodiment of the invention discloses a multi-sensor data fusion method and device, electronic equipment and a medium. One specific embodiment of the method comprises the following steps: acquiring a point cloud data set in a target area and an image shot by a vehicle-mounted monocular camera; performing feature extraction on the image to generate an image feature map; meshing the target areato generate a sub-target area set; determining a mean value of the point cloud data in each sub-target area to generate point cloud data mean value information; generating a point cloud data feature information set; determining mapping coordinates of the point cloud data corresponding to each piece of point cloud data feature information in the image feature map; generating splicing feature information; generating fusion feature information; generating obstacle detection information; and sending the obstacle detection information to a target display terminal and displaying the obstacle detection information on the target display terminal. According to the embodiment, the waste of computing resources is reduced, and the accuracy of obstacle information generation is improved.

Description

Technical field[0001]The embodiments of the present disclosure relate to the field of computer technology, in particular to multi-sensor data fusion methods, devices, electronic equipment and media.Background technique[0002]In the field of autonomous driving, different sensors have different advantages and limitations. Therefore, when detecting obstacles, a variety of sensors are often needed for cooperative sensing. At present, the existing technology often uses different sensing algorithms to detect obstacles on the information obtained by each sensor. Then, the recognized obstacle information is fused by a fusion algorithm. Furthermore, the final obstacle information is obtained.[0003]However, when the above-mentioned method is used for multi-sensor data fusion, the following technical problems often exist:[0004]First, it is necessary to use different perception algorithms to detect obstacles in the data obtained from different sensors, and then use the fusion algorithm to fuse t...

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Problems solved by technology

[0004] First, it is necessary to use different perception algorithms to detect obstacles on the data obtained from different sensors, and then use the fusion algorithm to fuse the obtained multiple obstacle information to generate the final obstacle information. This method requires multiple sets of Algorithms for data processing, the steps are cumbersome, and cause a waste of computing resources;

[0005] Second, when generating the final obstacle information, it is to fuse the obtained multiple obstacle information instead of fusing the data features. Since the obstacle information contains less data features, the final obstacle information may be The information is not accurate enough;

[0006] Third, in the actual situation, the point cloud data on the front and rear sides of the self-driving vehicle during driving is more important, and the point cloud data on both sides of the vehicle body is relatively less important. Point cloud data, without discarding relatively unimportant point cloud data, makes the calculation process contain more unimportant point cloud data, making the calculation efficiency not high enough

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