LiDAR Noise Point Detection Using Local Neighborhood Values
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Solution Overview
Problem
LiDAR systems face challenges in accurately identifying noise points in point cloud data due to interference from rain, fog, and dust, which degrades detection accuracy.
Innovation Solution
A method for detecting noise points in LiDAR data by determining a target determination value from multiple candidate values based on characteristic parameters of neighboring data points, using weighted distance differences and reflectivity values to distinguish between real and noise points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise filtering methods are used, then processing speed is maintained, but detection accuracy deteriorates due to inability to distinguish noise points from real objects in adverse weather
Solution Approach 1:
The patent segments the point cloud data by defining neighborhood windows around each data point, separating the processing into local regions. This allows independent analysis of each point's local environment, enabling accurate noise identification without requiring complex global processing.
Solution Approach 2:
The patent applies local quality by using characteristic parameters (distance, reflectivity, height) specific to each neighborhood window. The noise determination is based on local statistical properties rather than global thresholds, allowing adaptive noise filtering that preserves real objects while removing noise.
2Measurement precision
If multiple candidate determination values are calculated from neighborhood points, then noise point identification accuracy is improved, but computational burden increases
Solution Approach 1:
The patent calculates multiple candidate determination values (excessive action) by comparing each point with its M neighborhood points, but only uses the most reliable candidates for final determination. This partial use of multiple values achieves high accuracy while controlling computational burden.
Solution Approach 2:
The patent changes parameters by using different characteristic parameters (distance difference, reflectivity difference, height difference) to calculate multiple candidate determination values. This multi-parameter approach improves accuracy while the systematic calculation method controls computational complexity.
3Productivity
If noise points are identified using simple threshold methods, then processing efficiency is maintained, but detection accuracy deteriorates in heavy rain, fog, or dust conditions
Solution Approach 1:
The patent implements feedback by using the characteristic parameters of M neighborhood points to determine the target determination value for each point. This local feedback mechanism adapts to adverse weather conditions by comparing each point with its local environment, improving reliability while maintaining efficiency through systematic processing.
Solution Approach 2:
The patent performs preliminary action by pre-defining the neighborhood window size and characteristic parameters before noise detection. This preparation allows efficient processing during actual detection, as the framework is already established and only requires calculating determination values based on pre-specified criteria.
Data Source
AI summary
The present application provides a method, a detection device, a LiDAR, and a medium for detecting LiDAR noise points. The method includes: obtaining multiple candidate determination values according to the characteristic parameters of the current data point and the characteristic parameters of M data points in the neighborhood window of the current data point; determining the target determination value of the current data point from multiple candidate determination values; and determining whether the current data point is a noise point according to the target determination value.