Three-dimensional scanning coherent laser radar point cloud processing method based on velocity clustering statistics

Abstract

The invention discloses a high-precision three-dimensional scanning coherent laser radar point cloud processing method based on speed clustering statistics. Firstly, distance speed information and corresponding target position information of a moving target are obtained through a coherent laser radar and a three-dimensional scanner, and then a point cloud data set is formed through storage of a data storage unit. And finally, speed clustering statistics is performed on each data point in the point cloud data set. The clustered point cloud data set is counted in a plurality of speed subintervals. And speed layering processing is performed on all the speed subintervals, the motion state of the target is judged by comparing the size and the symbol of the characteristic speed value of the speed subintervals, and the accurate judgment of the motion attribute and the motion behavior of the target is realized through the speed. Compared with other processing methods, the method has the advantages of being accurate, effective, high in calculation speed, good in real-time performance and the like.

Description

technical field [0001] The invention relates to a three-dimensional scanning coherent laser radar, in particular to a point cloud processing method of a three-dimensional scanning coherent laser radar based on velocity clustering statistics. [0002] technical background [0003] As a high-precision sensor that can directly obtain the three-dimensional information of the target object, lidar is currently playing an increasingly important role in environmental perception such as unmanned driving technology, autonomous navigation, and remote sensing detection. Its measurement accuracy can reach centimeters. , and is less affected by the external environment. Generally speaking, the effective data obtained by lidar is mainly displayed in the form of point cloud in imaging fields such as unmanned driving technology. In the road environment, the target detection and recognition of lidar point cloud is mainly divided into two parts: target segmentation and target recognition. Sinc...

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Problems solved by technology

[0005] Aiming at the problems of complex algorithms and cumbersome procedures in the existing 3D scanning laser radar point cloud image segmentation scheme, the present invention proposes a point cloud processing method of 3D scanning coherent laser radar based on velocity clustering statistics. The method of clustering statistics, through the unique velocity measurement features in the 3D scanning coherent lidar point cloud data, the velocity is statistically classified

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