LiDAR Point Cloud Noise Filtering for Rain and Snow
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Solution Overview
Problem
Existing LiDAR systems struggle to effectively remove noise from point clouds acquired in snowy or rainy environments, leading to difficulties in object recognition and classification, and existing methods either fail to provide precise data analysis or require additional hardware and processing that is not real-time compatible.
Innovation Solution
A method and apparatus that checks LiDAR point distances, intensity measurements, and neighboring point densities to identify and remove noise points in real-time, using a noise intensity threshold and neighboring point count to distinguish between noise and environmental information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple filtering conditions with threshold standards are used to remove snow/rain noise, then noise removal speed is improved, but filtering precision deteriorates due to vulnerability to large-sized rain or snow
Solution Approach 1:
The patent changes the parameters used for noise detection from simple spatial thresholds to a combination of intensity values and spatial density. By introducing intensity measurement thresholds and neighboring point count thresholds, the system can differentiate between noise particles and actual objects more accurately, resolving the contradiction between fast processing and precise identification.
Solution Approach 2:
The patent replaces the mechanical threshold-based filtering approach with an intensity-based detection mechanism. Instead of relying solely on spatial position thresholds, the system uses LiDAR intensity measurements to identify noise, providing more accurate discrimination without sacrificing processing speed.
2Measurement precision
If additional hardware and complex processing algorithms are added to improve noise removal precision, then measurement precision is improved, but device complexity and processing time increase
Solution Approach 1:
The patent makes the LiDAR system perform multiple functions: it simultaneously measures distance, intensity, and spatial distribution of points. By utilizing the multi-dimensional data already captured by the LiDAR sensor, the system achieves precise noise removal without adding dedicated hardware for each measurement type, thus maintaining simplicity while improving precision.
Solution Approach 2:
The patent uses the LiDAR system's own inherent capabilities (intensity measurement and point cloud data) to identify and remove noise. The system serves itself by utilizing its existing data streams for noise detection, eliminating the need for external sensors or complex additional processing systems.
3Productivity
If traditional filtering methods are used in snowy or rainy environments, then processing speed is maintained, but object recognition accuracy deteriorates due to insufficient precise data analysis
Solution Approach 1:
The patent introduces intensity value parameters and neighboring point count parameters to enhance the traditional filtering approach. These additional parameters enable more reliable object recognition by providing better discrimination between noise and actual objects, while maintaining processing speed through efficient threshold-based evaluation.
Solution Approach 2:
The patent implements a feedback mechanism where the system evaluates multiple conditions (distance, intensity, neighboring points) and uses this feedback to make informed decisions about point classification. This multi-condition feedback approach improves recognition accuracy without significantly increasing processing time.
Data Source
AI summary
Disclosed is a method and apparatus for removing LiDAR point cloud noise. This method removes in real time only the LiDAR point cloud noise acquired in snowfall and rain environments, leaving behind the environmental characteristics of the LiDAR point cloud. Accordingly, it is possible to maximize the utilization of the LiDAR.