LiDAR Histogram Peak Filtering for Distance Accuracy
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Solution Overview
Problem
Existing LiDAR systems face performance limitations due to interference signals from glare, dust, and reflections, leading to distance detection errors, as current methods like median filtering, Gaussian filters, and signal-to-noise ratio calculations are inadequate in effectively removing these interference peaks and maintaining high-frequency depth information.
Innovation Solution
A LiDAR system configured with a laser source, optical module, pixel circuit, time-to-digital converter, memory device, and processor module, which transforms received signals into histograms to identify and filter peaks, using matching functions and feature vectors to enhance peak detection and eliminate interference, thereby improving distance measurement accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If median filtering or Gaussian filters are used to remove noise, then noise reduction is improved, but high-frequency depth information is lost and interference peaks remain
Solution Approach 1:
The patent segments the signal processing by separating noise filtering from peak detection. Instead of using traditional filters that smooth the entire signal, the system segments peaks based on their height and width characteristics, allowing selective processing that preserves high-frequency depth information while removing interference.
Solution Approach 2:
The patent applies local quality by using adaptive thresholding where the threshold for peak detection is determined locally based on the histogram data characteristics. This allows the system to maintain sensitivity to high-frequency depth information in certain regions while filtering interference peaks in other regions, rather than applying a uniform filtering approach.
2Reliability
If signal-to-noise ratio calculations are used to filter signals, then signal quality is improved, but interference peaks from glare and dust are not effectively removed
Solution Approach 1:
The patent implements dynamics by using adaptive thresholding where the threshold for distinguishing valid peaks from interference is not fixed but dynamically determined based on the histogram data characteristics. The system adjusts the threshold based on the distribution of signal values, allowing it to effectively separate interference peaks from valid distance measurements even in challenging conditions like glare and dust.
Solution Approach 2:
The patent changes parameters by considering both the height and width of peaks in the histogram, rather than relying solely on signal-to-noise ratio calculations. By introducing peak width as an additional parameter and using adaptive thresholding, the system can distinguish interference peaks (which often have different width characteristics) from valid distance measurements, improving distance detection accuracy.
3Productivity
If traditional peak detection methods are used, then processing speed is maintained, but distance detection errors occur due to interference peaks
Solution Approach 1:
The patent applies preliminary action by pre-defining threshold values and peak characteristics (height and width criteria) before processing the histogram data. This allows the system to quickly filter out interference peaks using simple comparison operations rather than complex calculations, maintaining high processing speed while improving distance detection accuracy by eliminating false peaks caused by glare, dust, and other interference.
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 system effectively removes invalid peaks, enhancing the accuracy and reliability of LiDAR systems by adapting to various scenarios, including glare, dust, and glass reflections, and maintaining high-frequency depth information, thus improving overall performance and precision.
Implementation Method 1
the optical module receives the reflected laser signal
Implementation Method 2
The TDC uses to generate histogram data. The processor module identifies and selects peaks from the histogram data, calculates a preliminary peak location associated with the target object distance
Data Source
AI summary
The present invention is directed to LiDAR systems and methods. In specific embodiments, the received signal is transformed into a histogram, facilitating the identification and filtering of one or more peaks to enhance accuracy. Various other embodiments are also provided, offering diverse solutions for optimizing LiDAR performance in different applications.