Airborne LiDAR Point Cloud Filtering Using Super-Voxel Ground Saliency
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Solution Overview
Problem
Existing LiDAR point cloud filtering methods struggle to provide universally applicable solutions for scenes with significant terrain feature variations, leading to erroneous filtering results.
Innovation Solution
A LiDAR point cloud filtering method based on super-voxel ground saliency, which constructs super-voxels from the input point cloud, divides them into scanning strips, computes saliency, and uses semi-global optimization to find the optimal filtering plane, enhancing the separation of ground and non-ground points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If regular grid-based filtering methods are used, then the filtering process is simple and fast, but the filtering accuracy deteriorates in scenes with significant terrain feature variations
Solution Approach 1:
The patent divides the point cloud into multiple scanning strips based on super-voxel segmentation, transforming the global filtering problem into multiple local optimization problems. This segmentation allows the algorithm to adapt to local terrain variations while maintaining computational efficiency through parallel processing of individual strips.
Solution Approach 2:
The patent implements direction-specific saliency computation for each scanning strip, allowing the filtering parameters and optimization criteria to vary locally according to the specific terrain characteristics of each strip. This local adaptation improves filtering accuracy for diverse terrain features while maintaining overall system efficiency.
2Adaptability or versatility
If existing filtering methods are applied to diverse terrain scenarios, then a single method can be used, but the filtering accuracy deteriorates due to inability to adapt to different terrain features
Solution Approach 1:
The patent creates a universal filtering framework that can handle diverse terrain types through super-voxel-based scanning strip division and direction-specific saliency computation. The method adapts to different terrain scenarios by automatically adjusting the scanning directions and saliency thresholds, making it versatile across various applications while maintaining high accuracy.
Solution Approach 2:
The patent implements dynamic adaptation by computing saliency values specific to each scanning direction and terrain scenario. The algorithm dynamically adjusts filtering parameters based on the local point cloud distribution and terrain characteristics, enabling it to adapt to diverse terrain features rather than using fixed parameters.
3Measurement precision
If more filtering parameters are input to improve accuracy, then the filtering precision improves, but the ease of operation deteriorates due to increased parameter input requirements
Solution Approach 1:
The patent implements self-service by automatically computing optimal filtering parameters through the saliency computation process. The algorithm derives direction-specific saliency values and optimization parameters directly from the point cloud data itself, eliminating the need for manual parameter tuning while maintaining high filtering accuracy.
Solution Approach 2:
The patent transforms fixed filtering parameters into dynamically computed saliency values that change based on the specific characteristics of each scanning strip. This parameter transformation allows the system to adapt to different terrains automatically, improving accuracy without requiring users to manually adjust multiple parameters.
Data Source
AI summary
An airborne LiDAR point cloud filtering method based on super-voxel ground saliency is provided. The method begins by denoising the input LiDAR point cloud, employing a boundary-preserving TBBP super-voxel algorithm for super-voxel segmentation. The super-voxels are categorized into scanning bands in N directions based on their centroid coordinates. Subsequently, each direction's scanning bands are sorted, segmented, and their saliency values are computed. Utilizing saliency values as coordinating factors, an energy function for the optimal filtering plane is constructed and minimized, leading to the determination of the optimal segmentation plane. Ground and non-ground points are classified based on the segmentation plane, thereby establishing the point cloud filtering results. The use of super-voxel-based saliency calculation in this invention enables preliminary vertical filtering of target point clouds, addressing the challenge of misclassifying point clouds with concave structures as ground points.