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Real-time ground detection method based on laser radar

A lidar, ground detection technology, applied in measurement devices, re-radiation of electromagnetic waves, radio wave measurement systems, etc., can solve the problems of slow segmentation, over-segmentation, under-segmentation, etc., and achieve the effect of high sensitivity

Inactive Publication Date: 2020-05-29
SHANGHAI UNIV OF ENG SCI
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  • Claims
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Problems solved by technology

[0008] The purpose of the present invention is to provide a real-time ground detection method based on laser radar in order to solve the existing problems of over-segmentation, under-segmentation, and slow segmentation based on laser radar point cloud ground segmentation.

Method used

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  • Real-time ground detection method based on laser radar
  • Real-time ground detection method based on laser radar
  • Real-time ground detection method based on laser radar

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Embodiment

[0034] as attached figure 1 to attach Figure 5 Shown:

[0035] figure 1 A method for real-time ground detection based on lidar provided in this embodiment includes the following steps:

[0036] 1) Scan the urban road environment information around the car with the self-driving car as the center, and input the point cloud data scanned by the laser radar in real time.

[0037] 2) The existence of invalid points is likely to cause errors in the extraction of feature points and the fitting of the plane. Therefore, according to the coordinates and intensity value information of the point cloud, it is judged whether it is an invalid point and eliminated.

[0038] 3) For the point cloud data after removing invalid points, the lidar is divided into 180 fan-shaped areas with 2° as the center, and each point is projected into the fan-shaped area where it is located.

[0039] 4) Filter the projected point cloud, and select the point P={p 1 ,p 2 ,...,p 180}.

[0040] 5) The remai...

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Abstract

The invention relates to a real-time ground detection method based on laser radar. The method comprises the following steps of S1, eliminating invalid points in a laser radar data point cloud set; S2,equally dividing a circle in the horizontal direction with the laser radar as the center into a plurality of fan-shaped areas, finding out a point with the minimum Z value projected in each fan-shaped area, selecting out points, with the height difference with the corresponding point with the minimum Z value in a set range, in each fan-shaped area, and classifying the points into a to-be-fitted point cloud set; S3, performing plane fitting on the to-be-fitted point cloud set to obtain a plane equation; and S4, carrying out cyclic traversal on the point cloud set obtained in the step S1, and comparing the Z value of the data point with the height of the fitting plane to realize ground detection. Compared with the methods in the prior art, the method can accurately detect the ground in realtime in an urban traffic scene.

Description

technical field [0001] The invention relates to a laser radar detection method in the field of automatic driving, in particular to a laser radar-based real-time ground detection method. Background technique [0002] With the rapid development of different types of sensing sensors such as three-dimensional lidar, millimeter-wave radar, and cameras, the environmental perception functions of self-driving cars have gradually matured, making autonomous driving slowly enter the public's sight. In the outdoor environment perception scene, the ground segmentation of lidar is an important preprocessing task of environment perception, and it is the basis of obstacle detection, classification and dynamic target tracking. It can help reduce the size of the data to be processed and further reduce the overall calculation time. . Due to the uneven distribution of 3D lidar data, that is, the point cloud near the lidar is relatively densely distributed, and the point cloud far away from the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S17/931G01S17/06
CPCG01S17/06
Inventor 周志峰庞正雅方宇吴明晖董浩张怡
Owner SHANGHAI UNIV OF ENG SCI