A point cloud filtering method based on Snake model multi-primitive fusion

Abstract

The invention discloses a point cloud filtering method based on Snake model multi-primitive fusion, By adopting the filtering strategy of multi-primitive fusion (point primitive and object primitive),the defect of poor robustness of single primitive filtering method can be effectively avoided, and the feature information provided by different primitive can be fully utilized to improve the adaptability of filtering method to complex terrain region. The Snake model is mainly constructed. By setting the target energy function with the ground point adsorption force, the model can take into account the local scale information and global scale information of the point cloud, and fully tap the automation potential of the point cloud data itself. According to this model, the filtering threshold is expressed as a linear function of terrain slope change, and the adaptive update of filtering threshold can be realized, which avoids the defect of poor filtering precision of single threshold and excessive parameter setting of multiple thresholds.

Description

technical field [0001] The invention relates to the technical field of geospatial information systems, in particular to a point cloud filtering method based on multi-element fusion of a Snake model. Background technique [0002] Airborne LiDAR (Light Detection And Ranging) technology provides a new observation method for obtaining high-temporal-resolution geospatial information. It is known as another technological revolution in the field of surveying, mapping and remote sensing since the Global Positioning System. Airborne LiDAR technology has been widely used in many fields of production and life, such as road extraction, power line inspection, vegetation parameter estimation, change detection, and urban 3D model building. To achieve the above point cloud post-processing application, a very critical step is point cloud filtering, that is, removing ground object points from the LiDAR point cloud and retaining terrain points. [0003] Point cloud filtering is the basic work...

AI Technical Summary

Problems solved by technology

[0008] (1) Most filtering methods cannot take into account the local scale information and global scale information of point clouds, making it difficult for filtering methods to obtain good filtering effects in some complex terrain areas (such as steep slopes, discontinuous terrain, houses built on slopes, etc.)

[0009] (2) The use of a single primitive to achieve point cloud filtering cannot fully exploit the characteristics of point cloud data, resulting in poor robustness of the filtering method and difficulty in adapting to complex terrain environments

[0010] (3) In order to be able to adapt to various terrain environments, most filtering algorithms often require complex parameter setting and threshold adjustment, which not only reduces the degree of automation of the algorithm, but also is not conducive to the implementation of filtering by inexperienced staff

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