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Road marking automatic detection and classification method based on mobile laser scanning point cloud

A road marking and laser scanning technology, applied in character and pattern recognition, instruments, computer parts, etc., to improve quality, accurately detect and classify, and avoid training sample collection

Inactive Publication Date: 2017-03-15
XIAMEN UNIV
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AI Technical Summary

Problems solved by technology

[0005] However, how to automatically extract terrain and feature features from high-density, high-precision mass mobile laser scanning point cloud data is a challenge for the development of point cloud post-processing technology.

Method used

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  • Road marking automatic detection and classification method based on mobile laser scanning point cloud
  • Road marking automatic detection and classification method based on mobile laser scanning point cloud
  • Road marking automatic detection and classification method based on mobile laser scanning point cloud

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Embodiment

[0059] see figure 1 , the invention discloses a method for automatic detection and classification of road markings based on mobile laser scanning point clouds, which includes the following steps:

[0060] S1. Road surface point cloud data segmentation

[0061] Road markings only exist on the road, while the original point cloud contains a large number of non-road points. In order to reduce the amount of data for subsequent processing, the road surface point cloud is firstly segmented. This step is specifically implemented through the following steps:

[0062] S11. Divide the original point cloud data into voxels with a side length of 5 cm, and then perform upward region growth until the upper boundary of the point cloud, thereby clustering the original point cloud into several tree structures. The so-called upward region growth means that during the growth process, each voxel can only grow to the nine adjacent voxels above it.

[0063] S12, judge each tree structure respec...

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Abstract

The invention discloses a road marking automatic detection and classification method based on a mobile laser scanning point cloud. The method comprises the following steps: S1, performing pavement segmentation on original point cloud data to obtain pavement point cloud data; S2, performing intensity correction on the pavement point cloud data by use of an incident angle; S3, performing binary processing on the pavement point cloud data, and extracting road marking points; S4, segmenting the road marking points to separate mutually independent road marking targets; S5, calculating feature parameters of the road marking targets; and S6, by use of the feature parameters, constructing a decision tree, and classifying the road marking targets. The method can rapidly and accurately perform automatic detection and classification of road marks from the mobile laser scanning point cloud, greatly reduces the data processing time and the labor cost, and effectively guarantees the traffic safety and the reliability of intelligent driving.

Description

technical field [0001] The invention relates to the fields of intelligent transportation systems and smart city construction, in particular to a method for automatic detection and classification of road markings based on mobile laser scanning point clouds. Background technique [0002] Unmanned vehicles are currently a research hotspot in the field of intelligent transportation. There are two main sources of information for driverless cars, one is the surrounding environment information perceived by sensors such as cameras and radars, and the other is pre-made high-precision road maps. When performing maneuvers such as changing lanes, driverless cars must know the existence of another lane in advance, and also need to know the lane type, width, speed limit and other information of the lane. Therefore, high-precision road maps are essential for driverless cars. [0003] Lane information is mainly represented by road markings, so the construction of high-precision road maps ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46
CPCG06V20/588G06V10/44
Inventor 李军张昊成程明王程
Owner XIAMEN UNIV
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