Multi-beam point cloud data denoising method considering terrain characteristics

Abstract

The invention discloses a multi-beam point cloud data denoising method considering terrain characteristics. A basic thought of the method is that a topological relation among point cloud data is established based on a KD index tree; near neighbor data of points fits local planes based on an RANSAC algorithm; the distances between point cloud and the local fitting planes are calculated; and denoising is performed based on a statistic analysis method. In addition, a pre-judgment is performed according to normal vector characteristics of adjacent planes before denoising to remove an obvious outlier surface, and the point cloud at an abrupt slope is reserved, thereby preventing excessive denoising. Through the method, near-surface noise and obvious outlier noise data in the multi-beam point cloud data can be removed, and information of edges and the like is better reserved; and the design scheme is optimized on the basis of ensuring the abovementioned effects, so that the executive efficiency is improved.

Description

technical field [0001] The invention relates to a method for denoising multi-beam point cloud data in consideration of terrain characteristics. Background technique [0002] The shipborne multi-beam bathymetry system can quickly obtain point cloud data containing information such as three-dimensional coordinates and echo intensity on the seabed surface, providing guarantee for generating high-precision DEM. Before building a DEM, the point cloud data must be denoised. Due to the diversity and complexity of the seabed topography, the noise is attached to the surface and is difficult to remove. Manually processing massive data consumes a lot of storage space and computing time, and may even be "erroneously deleted". At present, there are many studies on point cloud data denoising at home and abroad, but there are relatively few studies on multi-beam point cloud data with more noise. The existing technical solutions are difficult to remove near-surface noise, and cannot guaran...

AI Technical Summary

Problems solved by technology

Disadvantages: the calculation of filter factors is complicated, and the threshold is difficult to define

[0005] The third is Gaussian curvature filtering. The principle of this implementation scheme is: calculate the curvature value of the point cloud everywhere, and adopt different filtering schemes according to the change of the point cloud. The effect is better for sharp areas, but the curvature calculation of the point cloud It is more complicated. For the measured multi-beam point cloud data with more noise, the efficiency is low and the expected effect cannot be achieved.

Disadvantages: This type of method is difficult to distinguish near-surface noise data, and has high requirements for the selection of the threshold value. If the selection is looser, it is difficult to remove the noise well. The setting is harsh, which is easy to cause excessive removal, and for sparse non-noise points, it is difficult to distinguish

[0008] 1) Limitation of effect: the existing schemes are difficult to better distinguish the noise data near the surface, or cannot ensure that the terrain data is well preserved while removing the near-surface noise data; 2) Poor adaptability to terrain: the existing schemes are respectively applicable to Different point cloud data, difficult to adapt to terrain changes

3) It is difficult to achieve a balance between the denoising effect and the efficiency of the method: the method with high efficiency is difficult to guarantee the effect, and the method with better effect is often complex in calculation and difficult to guarantee the efficiency

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