Point Cloud Outlier Removal Using Separation Degree Thresholds
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Solution Overview
Problem
Conventional methods for removing point cloud outliers generated near object edges by laser scanners are inefficient, as they often misidentify both low-density and high-density points, leading to incomplete removal of outliers.
Innovation Solution
A method that selects representative points along an axis, calculates separation degrees, and determines outlier candidates based on threshold conditions to accurately identify and remove outliers near edges by analyzing the separation degrees between neighboring points and the laser scanner's parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional outlier removal methods are used (removing points with not more than a certain number of neighbors), then low-density outliers are removed, but low-density normal points are also removed and high-density outliers cannot be removed
Solution Approach 1:
The patent changes the parameter for outlier detection from point density (number of neighbors) to separation degree (distance/angle relationship between neighboring points). By calculating the separation degree between each point and its neighbors, and comparing it to threshold values, the method can distinguish outliers from normal points more accurately without misclassifying low-density normal points as outliers
Solution Approach 2:
The patent segments the point cloud data by arranging points along a first direction and selecting representative points (leading-side and trailing-side representative points). This segmentation allows for direction-specific outlier detection, where different threshold conditions can be applied to different directions, improving the ability to remove outliers while preserving normal points
2Area of stationary object
If laser scanner rotates to increase point cloud acquisition range, then more area is covered, but laser collides with edge multiple times generating multiple outliers
Solution Approach 1:
The patent implements a feedback mechanism by calculating separation degrees and comparing them against threshold conditions to identify and remove outliers. This feedback loop processes the point cloud data to detect edge-induced outliers and removes them, thereby improving the quality of point cloud data acquired through rotation without requiring changes to the scanning process itself
3Loss of information
If laser beam is irradiated to edge, then spatial information is obtained, but accurate distance calculation fails due to reflection from both edge and object behind
Solution Approach 1:
The patent extracts and removes the harmful effect of edge reflections by identifying points with abnormal separation degrees as outliers and removing them from the point cloud. This extraction process separates the accurate spatial information from the erroneous distance measurements caused by edge effects, preserving useful data while eliminating measurement errors
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively identifies and removes outliers near object edges, improving the accuracy of point cloud data by distinguishing between edge and surface points, even in high-density areas, and is applicable to both 2D and 3D laser scanning.
Implementation Method 1
a distance is calculated by measuring a time or topology for a laser transmitted to return after being reflected from an object
Implementation Method 2
a portion of the laser is reflected from the edge and another portion is reflected from an object behind the edge
Data Source
AI summary
Disclosed herein a method of removing a point cloud outlier and an apparatus implementing the method. The method includes: arranging a point cloud obtained from a laser scanner along at least a first direction; selecting, neighboring first-axis points, between which a separation degree satisfies an inspection start threshold condition, as a first leading-side representative point and a first trailing-side representative point; selecting a first leading-side outlier candidate and a first trailing-side outlier candidate based on a first leading-side separation degree and a first trailing-side separation degree; and determining the first leading-side outlier candidate and the first trailing-side outlier candidate as a first outlier point, when the number of the outlier candidates satisfies an allowable threshold condition.