Multiline laser radar mass point cloud data rapid and effective extraction and vehicle and lane line feature recognition method

Abstract

The unmanned vehicle acquires three-dimensional mass point cloud data (point cloud field) through multiline laser radar for hundreds of MB per second, and the data storage space and processing timeliness have extremely high requirement for the computing resource. The invention provides a multiline laser radar three-dimensional point cloud data rapid and effective extraction method without influencing vehicle and lane line feature recognition. The r layer point cloud data of the vehicle multilayer point cloud data in the area of interest of the unmanned vehicle acquired through multiline laser radar are extracted by adaptive distance. Besides, the invention also provides a lane line extraction method through the return light intensity based on distance and angle correction. The requirement of mass point cloud data processing for the computer hardware can be reduced, the storage space and cost can be saved, the timeliness of point cloud data processing can be accelerated and rapid and effective extraction and feature recognition of the vehicle and lane line point cloud data in the area of interest of the unmanned vehicle can be realized. The method is suitable for multiple urban roads so as to be high in anti-interference capability and great in algorithm robustness.

Description

technical field [0001] The present invention is a technology aimed at the field of environment perception of unmanned vehicles. The amount of three-dimensional point cloud data collected by multi-line laser radar rotation is massive, reaching hundreds of MB per second. Therefore, the present invention can reduce the requirement on computer hardware for massive laser radar point cloud data processing, reduce the consumption of computing resources, and save storage space and computing time. It can realize the rapid and effective extraction of multi-line lidar three-dimensional mass point cloud data without affecting the feature recognition of vehicles; in addition, the multi-line lidar effective data is based on the distance and angle corrected back light intensity to extract lane lines key technologies in the field. The invention is a fast and effective information extraction and processing technology. Background technique [0002] Real-time and accurate identification of t...

AI Technical Summary

Problems solved by technology

At present, the detection of lane markings is mainly based on the visual system, which has poor anti-interference ability and is easily affected by factors such as the environment; while the detection of lane markings based on multi-line radar not only has strong anti-interference ability, but is less affected by environmental factors and other factors. High precision and strong real-time performance

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