Driving area detection method, apparatus, device, and computer-readable storage medium

Abstract

The present application discloses a drivable area detection method, device, device and computer-readable storage medium, and relates to the technical field of autonomous parking. The specific implementation scheme is as follows: by acquiring the matching image and the target image captured by the vehicle-mounted monocular camera on the surrounding area of ​​the vehicle, where the matching image is the previous frame image that is continuous with the target image; Reconstruction to obtain the target 3D point cloud data corresponding to the target image, so as to reconstruct the 3D point cloud data of the continuous frame images collected by the monocular camera; then, according to the target 3D point cloud data, determine the nearest obstacle point in the shooting area of ​​the target image, And according to each nearest obstacle point, determine the drivable area in the shooting area of ​​the target image, without the need to collect three-dimensional images or a large number of image samples for machine learning, reduce the dependence on the image acquisition method, reduce the processing difficulty, and improve the detection efficiency. Accuracy and reliability.

Description

technical field [0001] The embodiments of the present application relate to the technical field of image processing, and in particular, to an autonomous parking technology. Background technique [0002] In order to avoid the collision between the body and the obstacle or beyond the road boundary, the vehicle needs to detect the drivable area during the process of autonomous driving and autonomous parking. [0003] At present, it is mainly based on supervised deep learning image detection or using 3D cameras to collect 3D point clouds for drivable area detection. However, image detection based on supervised deep learning requires a lot of manual annotation costs, and models trained on limited datasets are difficult to solve the generalization problem. The 3D camera has a complex structure, is difficult to manufacture, and has a high detection cost. [0004] It can be seen that the reliability of the existing drivable area detection method is not high enough. SUMMARY OF TH...

AI Technical Summary

Problems solved by technology

However, image detection based on supervised deep learning requires a lot of manual labeling costs, and the model trained on a limited data set is difficult to solve the generalization problem

However, the structure of the 3D camera is complicated, the manufacturing is difficult, and the detection cost is high.

[0004] It can be seen that the reliability of the existing drivable area detection method is not high enough

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