An Efficient Labeling Method for Combining Laser Point Cloud and Image

Abstract

The present invention proposes an efficient labeling method combining laser point cloud and image, which performs initial external parameter automatic calibration through planar checkerboard target image data and laser point cloud data, realizes pre-labeling through automatic segmentation algorithm, and combines a small amount of manual intervention to check and correct The image labeling information is further refined, and the 3D laser point cloud corresponding to the image labeling object is determined by back projection, and then the accurate 3D point cloud of the target to be marked is obtained by re-segmentation clustering and growth, and finally through the precisely matched 3D point cloud Cloud and image calibration objects are further optimized for external parameters; the efficient labeling method of the joint laser point cloud and image of the present invention does not require a lot of manual intervention, reduces the difficulty of laser point cloud labeling, improves labeling efficiency, and has higher labeling precision. Not only can the point-by-point category information of the laser point cloud be obtained, but also new labeling data such as joint labeling information of image and laser point cloud object level can be obtained.

Description

technical field [0001] The invention relates to the technical field of automatic driving, in particular to an efficient labeling method combining laser point clouds and images. Background technique [0002] In the field of unmanned driving technology, sensor fusion, especially low-level fusion, has become one of the effective technical solutions in unmanned driving technology, by identifying and estimating the semantics of key elements such as vehicles, lane lines, pedestrians, and traffic signs in the surrounding environment and geometric information to assist unmanned vehicles to perceive the environment and plan driving routes. The current mainstream method is to use lidar and cameras to perceive the environment. Lidar can obtain accurate geometric information of the observed environment, and cameras can obtain For images with rich texture and color information, more accurate environmental information can be obtained through the fusion of the two. [0003] This technolog...

AI Technical Summary

Problems solved by technology

[0003] This technology needs to rely on the deep learning network in practical application. However, training the deep learning network model based on low-level fusion data requires massive laser point cloud and image data sets that have been aligned in time and space and jointly labeled. Such data sets are currently It has not yet been formed, so it is of great significance to study new and effective tools for joint labeling of laser point cloud and image data sets

Images