LiDAR Signal Accumulation Thresholding for Accuracy
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Solution Overview
Problem
Existing neighborhood accumulation methods for LiDAR detection struggle to improve detection accuracy, particularly when the echo signal strength of a remote object is low, leading to reduced measurement range performance and detection accuracy.
Innovation Solution
A method for processing detection results in LiDAR systems, which involves determining a signal peak of a target pixel signal and accumulating it with neighboring pixel signals if the signal peak is not greater than a first detection threshold and the signals satisfy an accumulation condition, thereby improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If neighborhood accumulation method is used to improve measurement range, then detection range is improved, but detection accuracy deteriorates due to ranging deviations
Solution Approach 1:
The patent applies local quality by differentiating between strong and weak echo signals. For strong signals (above threshold), no accumulation is performed, preserving original detection accuracy. For weak signals (below threshold), neighborhood accumulation is applied to improve detection range. This localized application of accumulation based on signal strength resolves the contradiction by avoiding accuracy degradation for already-detectable signals while extending range for weak signals.
Solution Approach 2:
The patent introduces dynamic adjustment of accumulation behavior based on real-time signal evaluation. The detection threshold acts as a dynamic criterion that determines whether accumulation should be applied. This dynamic approach allows the system to adaptively switch between direct detection (for strong signals) and accumulated detection (for weak signals), thereby maintaining accuracy while extending measurement range.
2Reliability
If neighborhood accumulation is applied to weak signals, then detection capability is improved, but ranging deviation increases
Solution Approach 1:
The patent segments the detection process into two distinct paths: direct detection for strong signals and accumulated detection for weak signals. By using the detection threshold as a segmentation criterion, the system applies different processing strategies to different signal types. This segmentation ensures that accumulation-induced ranging deviations only affect weak signals that would otherwise be undetectable, while strong signals maintain their original precision.
Solution Approach 2:
The patent changes the detection parameter (signal threshold) to control the application of accumulation. By comparing the echo signal strength against a predetermined threshold, the system dynamically adjusts whether to apply accumulation. This parameter-based control allows the system to optimize between detection capability and ranging precision by applying accumulation only when necessary for weak signals.
Data Source
AI summary
A method for processing detection result includes determining a signal peak of a target pixel signal, and determining an accumulated signal by accumulating the target pixel signal based on a determination that the target pixel signal and neighboring pixel signals satisfy an accumulation condition and the signal peak of the target pixel signal is not greater than a first detection threshold.