Seabed obstacle identification method based on multi-beam point cloud data and related equipment

By using an automated denoising method based on the elevation difference histogram and attenuation model of multibeam point cloud data, the problems of low filtering efficiency and incomplete recognition in multibeam point cloud data processing are solved, and efficient and reliable obstacle recognition is achieved.

CN120953117AInactive Publication Date: 2025-11-14GUANGZHOU MARINE GEOLOGICAL SURVEY SANYA SOUTH CHINA SEA INST OF GEOLOGY
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Patent Information

Application Number
CN202511483891.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-17
Publication Date
2025-11-14
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In existing technologies, multibeam point cloud data processing suffers from problems such as low filtering efficiency, low automation, incomplete sparse filtering of edge beam point clouds, easy deletion of real terrain points, and severe loss of terrain details.

Method used

By acquiring multibeam point cloud data, an elevation difference histogram is obtained based on the elevation difference statistics of adjacent sounding point clouds. An attenuation model is fitted to determine the noise threshold. The point cloud data is divided into real terrain point clouds and noise point clouds, and obstacle identification is performed using elevation slope.

Benefits of technology

It achieves a highly efficient and automated denoising process, improves data processing efficiency, reduces the accidental deletion of real terrain points and the loss of terrain details, and ensures the reliability and accuracy of obstacle recognition.

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Abstract

The invention discloses a seabed obstacle identification method based on multi-beam point cloud data and related equipment. The method comprises the following steps: acquiring the multi-beam point cloud data of a target area; performing statistics on the basis of the elevation difference between the adjacent sounding point clouds to obtain a frequency-based elevation difference histogram; fitting an attenuation model according to the elevation difference histogram, determining a noise threshold value based on the attenuation model, and dividing the multi-beam point cloud data into a real terrain point cloud and a noise point cloud by using the noise threshold value; a multi-beam real terrain point cloud is obtained through iterative denoising division; and based on the multi-beam real terrain point cloud, obstacle identification is carried out by using the elevation slope, and obstacle information of the target area is obtained. According to the method, through the automatic processes of determining the noise threshold value through the fitting attenuation model and iterative denoising, a traditional interactive filtering method depending on experience of technicians is replaced, the data processing efficiency is greatly improved, and the method can be widely applied to the technical field of data processing.
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