LiDAR Reception-Intensity Filtering for Changing Light Noise
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Solution Overview
Problem
Conventional LiDAR sensors face challenges in accurately canceling noise, particularly from sunlight and other environmental interferences, which affect the precision of point cloud data due to varying light conditions.
Innovation Solution
A LiDAR noise canceling device that utilizes reception intensity information of laser pulses to filter out abnormal pixels by comparing intensity data across adjacent pixels, over time, and using predicted intensity values to enhance noise cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional spatial filter or temporal filter is used to cancel noise, then device complexity is reduced, but noise cancellation effectiveness deteriorates under varying light conditions
Solution Approach 1:
The patent changes the parameter used for filtering from spatial/temporal relationships alone to include reception intensity information. The filter unit compares reception intensity values between adjacent pixels and across time points, using intensity differences as the key parameter to identify and remove noise points, thereby adapting to varying light conditions without increasing device complexity
Solution Approach 2:
The patent replaces the conventional mechanical/optical filtering approaches (spatial filters using pixel comparison, temporal filters using frame comparison) with an intensity-based filtering mechanism. Instead of relying on positional or temporal relationships alone, the system substitutes the filtering criterion with reception intensity analysis, comparing intensity values to detect and eliminate noise
2Measurement precision
If point cloud filtering is performed to remove noise, then measurement precision improves, but loss of information increases due to potential removal of valid data
Solution Approach 1:
The patent implements a feedback mechanism where the filter unit continuously compares reception intensity information with reference values (average intensity, adjacent pixel intensity, or temporal intensity patterns). This feedback loop allows the system to dynamically adjust filtering decisions, comparing each point's intensity against multiple reference criteria to distinguish noise from valid data, thereby maintaining measurement precision while minimizing information loss
Solution Approach 2:
The patent applies partial filtering by using multiple comparison criteria (adjacent pixel intensity, temporal intensity patterns, average intensity) rather than applying a single aggressive filtering rule. This partial action approach filters only those points that fail multiple intensity-based checks, reducing the risk of removing valid data while still achieving noise removal and maintaining point cloud accuracy
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
The device effectively filters noise from LiDAR point clouds, improving accuracy and consistency regardless of seasonal or weather changes, by leveraging intensity information for precise noise detection and removal.
Implementation Method 1
a light detection and ranging (LiDAR) noise canceling device that cancels noise by using intensity information of a LiDAR reception signal
Implementation Method 2
The ToF method is a method of measuring a distance by emitting the pulse signal from the laser and measuring a time of the pulse signal being reflected and returned from objects
Data Source
AI summary
The present disclosure relates to light detection and ranging (LiDAR) noise canceling device and method, and more particularly, to a LiDAR noise canceling device that cancels noise by using intensity information of a LiDAR reception signal and its method.