Differential Lidar Channel Filtering for Atmospheric Scattering Detection
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Solution Overview
Problem
Current lidar systems are limited to localized visibility estimation due to the use of short-range sensors, resulting in inadequate mid to long-range visibility estimation and impaired performance in atmospheric scattering conditions.
Innovation Solution
Utilizing differential analysis between interleaved lidar channels with varying sensitivity thresholds to detect atmospheric scattering media, enabling estimation of visibility and impairment detection in medium to long-range environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If short-range lidar sensors are used for visibility estimation, then the device complexity is reduced and ease of operation is improved, but the measurement precision and detection range are limited to localized areas only
Solution Approach 1:
The patent divides the lidar system into multiple channels with different sensitivity thresholds. Each channel is segmented to detect specific ranges of return light intensities, allowing the system to simultaneously monitor both strong and weak returns. This segmentation enables extended detection range and improved visibility estimation accuracy without requiring a completely new sensor design.
Solution Approach 2:
Different channels are assigned different sensitivity thresholds tailored to their specific detection needs. The first group of channels uses a first threshold optimized for detecting strong returns, while the second group uses a second threshold for weak returns. This local quality approach allows each channel to be optimized for its specific function, improving overall measurement precision across different ranges.
2Measurement precision
If multiple lidar channels with different thresholds are implemented, then the detection range and visibility estimation capability are improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the lidar channels into distinct groups based on their threshold characteristics. This segmentation simplifies the management of complexity by organizing channels into functional units that can be processed independently. The first group handles strong returns while the second group handles weak returns, allowing for modular data processing approaches.
Solution Approach 2:
The patent applies thresholds selectively to specific groups of channels rather than uniformly to all channels. This partial action approach reduces the overall processing burden by only applying thresholding where necessary. The first group of channels has a first threshold applied, while the second group has a different threshold, allowing the system to process data efficiently while maintaining extended detection capabilities.
3Reliability
If differential analysis between channel groups is performed, then atmospheric scattering medium detection capability is enhanced, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts the differential signal between the two channel groups as a separate detectable quantity. By taking out the difference between returns from the first group and returns from the second group, the system isolates the atmospheric scattering effect from the overall return signal. This extraction approach simplifies the detection process by focusing computational resources on the specific differential metric that indicates atmospheric conditions.
Solution Approach 2:
The differential between channel groups serves as an intermediary metric that mediates between the raw lidar returns and the atmospheric scattering detection. Rather than directly analyzing complex return patterns from multiple channels, the system uses the differential as an intermediate representation that simplifies the detection task while maintaining reliability in atmospheric scattering medium identification.
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 visibility estimation and impairment detection capabilities by leveraging differential metrics across lidar channels, providing accurate environmental assessment and spatial resolution of atmospheric scattering media.
Implementation Method 1
one or more light emitters emit light into an environment surrounding the lidar device, and one or more light detectors may detect reflected light
Implementation Method 2
Based on time differences between light emissions and receiving the reflected light, the lidar device can generate data
Implementation Method 3
Atmospheric scattering media that affect visibility in the environment, such as rain, fog, and snow
Data Source
AI summary
Example embodiments relate to differential methods for determining environment estimation, lidar impairment detection, and filtering. An example embodiment includes dividing a plurality of lidar device channels into a first group and a second group and interleaving the channels. The embodiment includes applying a threshold to the first group. The embodiment further includes emitting light pulses from a lidar device into an environment surrounding the lidar device, and detecting return light pulses. The return light pulses in the first group of channels are sampled from the signals that exceed the threshold. The embodiment may further include determining a differential in a statistical distribution between the return light pulses in the first group and the return light pulses in the second group. Based on the differential, the method can include detecting an atmospheric scattering medium in the environment surrounding the lidar device.