Lidar Scan Comparison for Saturation Recovery Range Errors
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Solution Overview
Problem
LIDAR devices experience range walk errors due to intensity-dependent measurement inaccuracies and saturation recovery periods, leading to degraded range measurement accuracy.
Innovation Solution
The method involves repeatedly scanning a range of angles in the field-of-view (FOV) of a LIDAR device, comparing scans to detect onset of saturation recovery periods, and adjusting time-of-flight computations and point cloud representations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lidar systems are used, then the system structure remains simple, but the measurement accuracy degrades over time due to environmental factors
Solution Approach 1:
The system performs preliminary calibration using calibration targets at multiple known distances before operation. This preliminary action establishes a baseline relationship between lidar return times and actual distances, allowing the system to detect and compensate for accuracy degradation over time without requiring complex real-time adjustments.
Solution Approach 2:
The system continuously monitors measurement accuracy by comparing lidar-derived distances against known distances from calibration targets. This feedback mechanism enables the system to detect drift in measurement accuracy and trigger recalibration or adjustment protocols, maintaining reliable measurements throughout operation.
2Measurement precision
If calibration targets are placed at multiple known distances, then measurement accuracy can be verified, but the system complexity increases
Solution Approach 1:
The calibration targets serve multiple functions: they act as reference objects for accuracy verification, provide known distance references for calibration, and can be integrated into existing operational environments. This multi-functionality reduces the need for separate calibration equipment and simplifies the overall system architecture.
Solution Approach 2:
The calibration process is segmented into discrete measurement points at known distances. By dividing the calibration task into separate distance points, the system can systematically verify accuracy across different ranges without requiring a single complex calibration procedure, making the process more manageable and less complex.
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
This approach enhances LIDAR range measurement accuracy by identifying and mitigating errors during saturation recovery periods, improving the reliability of point cloud data generation.
Implementation Method 1
a second processor to process the return signals and determine a time of flight for each return signal
Implementation Method 2
detecting degraded lidar range measurement accuracy...transmitting a plurality of laser pulses...return signals
Data Source
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AI summary
One example method involves repeatedly scanning a range of angles in a field-of-view (FOV) of a light detection and ranging (LIDAR) device. The method also involves detecting a plurality of light pulses intercepted for each scan of the range of angles. The method also involves comparing a first scan of the range of angles with a second scan subsequent to the first scan. The method also involves detecting onset of a saturation recovery period of the light detector during the first scan or the second scan based on the comparison.