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Multi-scale point cloud noise detection method based on density analysis

A technology of noise detection and density analysis, applied in the field of surveying and mapping science, which can solve the problems of different performances, difficult to detect, large differences in point cloud noise categories, etc.

Inactive Publication Date: 2014-12-24
CHINESE ACAD OF SURVEYING & MAPPING
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Problems solved by technology

[0005] The purpose of the present invention is to provide a multi-scale point cloud noise detection method based on density analysis, which can effectively detect the isolated noise and cluster noise contained in the point cloud obtained by airborne lidar data and the point cloud obtained by matching the image, thereby To overcome the problem that different types of point cloud noises have large differences and different performances, which are difficult to detect. This method analyzes the point cloud through multi-scale density, gradually eliminates the noise contained in the point cloud data, and finally builds a triangulation network and uses the triangulation network Constraints reduce the noise points that were falsely detected in the previous step

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[0032] The present invention provides a multi-scale point cloud noise detection method based on density analysis, such as figure 1 As shown, the method includes the following steps:

[0033](1) Input point cloud data and set the maximum scale;

[0034] (2) Use spatial hexahedrons to segment point cloud data in three dimensions, establish discrete point cloud three-dimensional grids and maximum range bounding boxes; use local density analysis methods to mark noise points as 0 and eliminate them to obtain new non-noise bounding boxes;

[0035] (3) On the basis of the non-noise bounding box obtained in the previous step, reduce the scale level by one level, and analyze the local density of the neighborhood one by one with 1 / 2 times the size of the three-dimensional grid, and further eliminate smaller noise points or noise points Cluster and mark as 0 to obtain a new non-noise bounding box; proceed iteratively until the minimum scale level noise point detection is completed;

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Abstract

The invention provides a multi-scale point cloud noise detection method based on density analysis. The method mainly comprises the steps that firstly, a multi-scale density analysis algorithm is utilized for preliminarily judging points which possibly are noise; secondly, triangulation network constrains are utilized for classifying the points which are detected as noise in the last step by mistake to be non-noise points again. The multi-scale point cloud noise detection method based on density analysis can effectively detect out isolated noise and cluster noise contained in point cloud of laser radar and contained in point cloud obtained through image matching, and has the good application prospect in the fields of laser radar point cloud filter and image matching mistake-matching point detection, bundle adjustment noise detection and the like.

Description

technical field [0001] The invention belongs to the field of surveying and mapping science and technology, and is mainly applied to the detection of laser radar point cloud noise and the detection of matching point cloud noise, and in particular relates to a multi-scale point cloud noise detection method based on density analysis. Background technique [0002] The processing of point cloud data has always been an important research content of digital photogrammetry and computer vision. At present, there are two main ways to obtain dense 3D point clouds: 1) using LiDAR (Light Detection And Ranging) system (and lidar system) to directly obtain 3D point clouds; 2) using image matching to obtain point clouds. However, there are a certain number of noise points in the point cloud data from both sources. The presence of noise has many effects on the processing of point cloud data. For example, in the LiDAR point cloud filtering process, many algorithms assume that the ground poi...

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Application Information

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IPC IPC(8): G06T7/00G06T5/00
Inventor 朱俊锋张力熊小东艾海滨杜全叶许彪
Owner CHINESE ACAD OF SURVEYING & MAPPING
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