Point Cloud Unwanted Point Removal Vertical Axis Grouping
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Solution Overview
Problem
Existing 3D coordinate measurement devices struggle to accurately generate digital representations of environments by removing unwanted points from point clouds, particularly in scenarios like construction sites where extraneous objects are scanned.
Innovation Solution
A method and system for removing unwanted points from point clouds by grouping points based on frequency of occurrence relative to a vertical axis, identifying upper and lower boundaries, and generating a revised point cloud that excludes points above the upper boundary and below the lower boundary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If laser scanners scan construction sites and other complex environments, then comprehensive environmental data is captured, but unwanted points from extraneous objects contaminate the point cloud
Solution Approach 1:
The patent extracts and removes unwanted points from the point cloud by analyzing their vertical distribution characteristics. Points outside the identified vertical range (above upper boundary or below lower boundary) are extracted and eliminated, separating useful environmental data from contaminating extraneous object data.
Solution Approach 2:
The patent segments the point cloud into valid environmental points and unwanted points based on vertical position. By dividing the vertical space into acceptable ranges (between upper and lower boundaries) and excluded ranges (above or below boundaries), the system segments the data for selective retention and removal.
2Measurement precision
If points are removed from point cloud to improve accuracy, then registration precision improves, but processing complexity increases
Solution Approach 1:
The patent replaces complex manual filtering methods with an automated computational approach using vertical frequency analysis. Instead of manual point-by-point evaluation, the system uses algorithmic analysis of vertical axis frequency to automatically identify and remove unwanted points, simplifying the processing workflow.
Solution Approach 2:
The patent changes the approach from spatial filtering to frequency-based filtering along the vertical axis. By transforming the selection criteria from geometric position to frequency of occurrence relative to the vertical axis, the system achieves more effective unwanted point removal with simpler processing logic.
Data Source
AI summary
An example method for removing an unwanted point from a point cloud is provided. The method includes grouping points of the point cloud based on a frequency of occurrence of the points relative to a vertical axis defined for an environment. The method further includes identifying a first set of points corresponding to an upper boundary of the environment and identifying a second set of points corresponding to a lower boundary of the environment. The method further includes generating a revised point cloud. The revised point cloud excludes points above the upper boundary and points below the lower boundary.