A LiDAR point cloud reflection intensity complement method and system

Abstract

The invention discloses a laser radar point cloud reflection intensity complement method and system. The method includes: using a calibrated vehicle camera and a laser radar to obtain a grayscale image and an original point cloud of the same road surface; using a preset edge The extraction strategy extracts the edge information of the grayscale image to obtain the edge image of the grayscale image; preprocesses the original point cloud to obtain the original point cloud reflection intensity projection image and interpolation complement point cloud reflection intensity projection image; the grayscale image , the edge image of the grayscale image, the original point cloud reflection intensity projection image and the interpolation complement point cloud reflection intensity projection image, input the pre-trained point cloud reflection intensity completion model, and output the complementary point cloud reflection intensity projection image. The point cloud reflection intensity complement method provided by the present invention can make full use of the potential correlation between the point cloud and the image, thereby effectively and accurately complementing the reflection intensity image of the lidar point cloud.

Description

technical field [0001] The invention belongs to the technical field of intelligent driving, and in particular relates to a method and system for complementing reflection intensity of laser radar point clouds. Background technique [0002] Intelligent driving vehicles need to sense and measure the surrounding environment through lidar to ensure safe driving, including lane line detection, object detection and tracking, etc. However, the number of scanning lines of the lidar limits the density of the recorded point cloud, and the sparse point cloud is difficult to use for high-precision calculations, so it is necessary to complete the sparse point cloud into a dense point cloud. The main difficulties of point cloud completion include: 1) the original point cloud is too sparse; 2) outdoor scenes lack annotations for neural network supervised learning. [0003] The existing methods are mainly aimed at the completion of point cloud depth information, but the completion of point ...

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Problems solved by technology

[0003] The existing methods are mainly aimed at the completion of point cloud depth information, but the completion of point cloud reflection intensity has some difficulties that depth estimation does not have: 1) The reflection intensity is related to the material of the object, and has little to do with the geometric information. The reflection intensity of different parts may be different; 2) The accuracy of the graphic segmentation label is not enough, and it is difficult to directly use it as a supervision label

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