LiDAR Histogram Analysis for Fog Interference Removal
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Solution Overview
Problem
Existing LiDAR systems face significant performance deterioration in foggy or rainy conditions due to light absorption or scattering, leading to reduced distance measuring accuracy and reliability.
Innovation Solution
An information processing apparatus that generates a histogram from received light signals, detects peaks, and judges the presence of light propagation interference factors like fog or rain. This apparatus then deletes interference-related frequencies, allowing for accurate distance measurement by detecting peaks in the cleaned histogram.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If infrared light is used for LiDAR sensors, then the sensor can operate in various lighting conditions, but the distance measurement accuracy deteriorates in foggy or rainy conditions due to light absorption and scattering by moisture
Solution Approach 1:
The patent segments the received light signals into multiple components by generating a histogram of light reception times and identifying multiple peaks. Each peak corresponds to different light paths (direct reflection from target, scattered light from fog/rain particles, ambient light). By separating these components, the system can selectively use only the direct reflection data for distance measurement, eliminating the harmful effects of scattered light in adverse weather conditions.
2Adaptability or versatility
If a multi-echo function is used to select true reflected light peaks from multiple reflected light peaks, then distance measurement can be performed in complex environments, but the function fails when the intensity of light reflected on fog or rain particles is large, decreasing measurement accuracy
Solution Approach 1:
The patent introduces a new dimension for analyzing light signals by creating a histogram distribution of light reception times rather than simply comparing peak intensities. This dimensional transformation allows the system to identify and separate different light components based on their temporal distribution patterns. The histogram approach reveals multiple peaks corresponding to different light paths, enabling the system to distinguish true target reflections from fog/rain scattered light even when the scattered light intensity is high.
3Productivity
If simple methods are used to judge environmental state, then the system can quickly determine fog or rain conditions at low cost, but existing methods cannot effectively distinguish true target reflection from scattered light in adverse weather
Solution Approach 1:
The patent employs self-service by using the LiDAR system's own received light signals to automatically detect and characterize adverse weather conditions. The histogram analysis of light reception times inherently reveals the presence of fog or rain through the appearance of additional peaks corresponding to scattered light. This self-diagnostic capability eliminates the need for separate, complex environmental sensors while providing accurate weather condition detection and simultaneous distance measurement correction.
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 solution effectively improves the accuracy and reliability of distance measurements in adverse weather conditions by isolating and removing interference factors, thereby maintaining high success rates even in foggy or rainy environments.
Implementation Method 1
a light receiver 7 configured to receive the reflected light signals
Implementation Method 2
infrared light is likely to be absorbed or scattered by moisture
Implementation Method 3
infrared light is likely to be absorbed or scattered by moisture
Data Source
AI summary
An information processing apparatus has processing circuitry configured to generate a histogram indicating a relationship between a light reception time and a light reception frequency based on a plurality of received signals including reflected light from an object, detect a peak of the light reception frequency of the histogram, and judge whether there is a light propagation interference factor including at least one of fog, mist, haze, rain, and snow based on the histogram and the light reception time of the peak, wherein it is judged whether there is the light propagation interference factor based on the light reception time of the peak and a degree of change in the light reception frequency of the histogram between before and after the light reception time of the peak.