Lidar Saturated Signal Edge Detection for Nanosecond Precision
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Solution Overview
Problem
Traditional LIDAR systems face inaccuracies in distance measurement due to distortions from highly reflective surfaces and close objects, leading to nanosecond variations in return signal time, which are difficult to detect, especially in high-noise conditions, resulting in reduced accuracy and filtered-out weaker return signals.
Innovation Solution
The implementation of a LIDAR system with multiple detectors that classify return signals as saturated or unsaturated, selecting the appropriate detector output for distance measurement, and dynamically adjusting the noise floor to discriminate active signals from noise, improving accuracy and range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional threshold filtering is used to eliminate noise, then noise is filtered out, but weaker return signals are also filtered out, reducing LIDAR range
Solution Approach 1:
The patent implements dynamic threshold adjustment based on the detected signal characteristics and noise conditions. The threshold is not fixed but adapts in real-time to distinguish between noise and weak return signals, allowing the system to maintain noise rejection while preserving detection of faint signals from distant objects.
Solution Approach 2:
The system changes the threshold parameter dynamically based on environmental conditions and signal properties. By adjusting the threshold level adaptively rather than using a static value, the system can differentiate between noise and legitimate weak return signals, resolving the contradiction between noise filtering and signal preservation.
2Object-affected harmful factors
If high threshold is set to filter noise, then noise is reduced, but the range of the LIDAR system is reduced
Solution Approach 1:
The threshold is made dynamic and adaptive rather than fixed. The system continuously monitors signal characteristics and adjusts the threshold accordingly, allowing it to operate effectively across varying noise conditions while maintaining maximum detection range by lowering the threshold when weak signals are present.
3Ease of operation
If conventional peak detection is used for saturated signals, then distance measurement is simplified, but nanosecond variations in return signal time go undetected, reducing accuracy
Solution Approach 1:
The system performs preliminary classification of signals to identify saturated signals before attempting distance measurement. By detecting saturation conditions in advance, the system can then apply appropriate processing methods that preserve temporal information, preventing loss of nanosecond-scale timing data that would occur with conventional peak detection.
Solution Approach 2:
The patent introduces an intermediate classification step that identifies saturated signals and routes them through a specialized processing path. This intermediary classification mechanism allows the system to handle saturated signals differently, preserving the temporal information needed for accurate time-of-arrival measurement while still simplifying the overall process through automated classification.
4Measurement precision
If multiple detectors are used to handle saturated and unsaturated signals, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The detection system is segmented into specialized detectors for different signal types (saturated vs. unsaturated). Each detector is optimized for its specific signal type, improving measurement accuracy for each category while maintaining overall system manageability through functional segmentation.
Solution Approach 2:
The system employs a universal classification mechanism that routes signals to appropriate detectors based on their characteristics. This multi-functional approach allows a single classification system to handle both saturated and unsaturated signals, coordinating multiple detectors through a unified control structure that manages complexity.
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
Enhances the accuracy of distance determination to within a third of a nanosecond, improving safety and accuracy for autonomous vehicles and robotic movements by effectively handling saturated signals and noise variations.
Implementation Method 1
The light emitter may comprise a laser that directs light into an environment. When the emitted light is incident on a surface, a portion of the light is reflected and received by the light sensor, which converts light intensity to a corresponding electrical signal.
Data Source
AI summary
A time delay of arrival (TDOA) between a time that a light pulse was emitted to a time that a pulse reflected off an object was received at a light sensor may be determined for saturated signals by using an edge of the saturated signal, rather than a peak of the signal, for the TDOA calculation. The edge of the saturated signal may be accurately estimated by fitting a first polynomial curve to data points of the saturated signal, defining an intermediate magnitude threshold based on the polynomial curve, fitting a second polynomial curve to data points near an intersection of the first polynomial curve and the intermediate threshold, and identifying an intersection of the second polynomial curve and the intermediate threshold as the rising edge of the saturated signal.