LIDAR Beat-Frequency Refinement for Edge Error Detection
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Solution Overview
Problem
LIDAR systems suffer from errors in data generation due to the system output signal illuminating one object at the start of a regional time period and then moving to illuminate another object before the period expires, leading to unreliable data.
Innovation Solution
A LIDAR system that changes the frequency of its output signal in a series of cycles, combines the returned light with a reference signal to generate a beating signal, and uses electronics to identify edge detection and outlier patterns in the beat frequency change data to correct LIDAR data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the LIDAR system scans the output signal across multiple sample regions during a regional time period, then the productivity of data generation is improved, but the reliability of LIDAR data deteriorates due to edge errors when the signal illuminates different objects within the same time period
Solution Approach 1:
The system performs preliminary actions by continuously scanning and collecting LIDAR data from multiple sample regions before the regional time period expires. The electronics store this preliminary data and use it to identify edge errors through frequency change analysis, allowing the system to maintain high productivity while preparing correction data in advance.
Solution Approach 2:
The system implements feedback by analyzing the frequency change of the output signal across different sample regions and using this information to identify edge errors. The electronics compare frequency data from consecutive sample regions and adjust or flag data accordingly, creating a feedback loop that improves reliability while maintaining the scanning productivity.
2Area of stationary object
If the system output signal illuminates multiple objects during a single regional time period, then the measurement coverage is improved, but the measurement precision deteriorates due to edge detection errors
Solution Approach 1:
The system segments the continuous LIDAR data stream into discrete sample region measurements, each associated with a specific frequency range. The electronics analyze frequency changes between adjacent segments to identify edge errors, allowing precise detection and correction while maintaining comprehensive scene coverage through the segmented approach.
Solution Approach 2:
The system exploits parameter changes by monitoring the frequency of the output signal as it scans across different sample regions. The electronics detect edge errors by identifying abnormal frequency changes between consecutive regions, using this parameter variation to maintain measurement precision across the entire scanned scene.
3Productivity
If the LIDAR system continuously scans during the regional time period, then the productivity is improved, but the reliability deteriorates because the signal may illuminate different objects causing data errors
Solution Approach 1:
The system uses feedback by continuously monitoring the frequency of the output signal during scanning and comparing it with expected frequency ranges for each sample region. When the electronics detect frequency deviations indicating the signal has moved to a different object, they can flag or correct the data, maintaining reliability while preserving continuous scanning productivity.
Solution Approach 2:
The system performs self-service by automatically detecting and correcting its own edge errors through frequency analysis. The electronics monitor the output signal frequency changes and autonomously identify when the signal illuminates different objects, correcting the data without external intervention and maintaining both productivity and reliability.
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 generates more reliable LIDAR data by identifying and correcting edge errors and outliers, suitable for rapid data generation in applications like advanced drive assistance systems and autonomous vehicles.
Implementation Method 1
combines light that returns to the LIDAR system from the system output signal with light from a reference signal so as to generate a beating signal beating at a beat frequency
Data Source
AI summary
The optical system is configured to output a system output signal such that a frequency of the system output signal changes in a series of repeated cycles. Each of the cycles includes multiple data periods. The frequency of the system output signal changes at different rates during different data periods. The optical system includes a light-combining component that combines light that returns to the optical system from the system output signal with light from a reference signal so as to generate a beating signal beating at a beat frequency. The system includes electronics that generate frequency change data that indicates a beat frequency change over time. The electronics can apply edge detection criteria and/or outlier detection criteria to the frequency change data.