Geiger-mode LADAR Real-time Image Formation via 3D Gaussian Filtering
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Solution Overview
Problem
Geiger-mode laser radar sensors face challenges in transforming data from a row:column:time format to an XYZ coordinate system and combining multiple frames while correcting for detector saturation and blocking loss, which distorts range profiles and affects signal-to-noise ratio.
Innovation Solution
A 3D Gaussian filter is applied using a graphics processing unit to convert photo-events from two spatial and one temporal coordinate to three spatial coordinates, incorporating a Gaussian displacement list and weight lists for blocking loss compensation, resulting in real-time image formation and improved fidelity of measured profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple frames of Geiger mode LADAR data are combined to increase signal-to-noise ratio, then measurement precision is improved, but device complexity increases due to the need for real-time processing and correction algorithms
Solution Approach 1:
The patent segments the complex processing task into distinct computational stages: photo-event data acquisition, 3D Gaussian filtering, blocking loss correction, and image aggregation. Each stage processes specific aspects of the data independently, allowing for optimized computation and reducing overall system complexity while maintaining measurement precision through systematic multi-frame combination
Solution Approach 2:
The patent introduces intermediate data structures and processing representations, including 3D Gaussian filter kernels and blocking loss compensation factors, that mediate between raw photo-event data and final images. These intermediaries enable complex corrections to be applied in a manageable, stepwise manner that reduces computational burden while improving measurement precision
2Productivity
If 3D Gaussian filtering is applied to convert photo-events to XYZ coordinates in real-time, then image formation speed is improved, but processing complexity increases
Solution Approach 1:
The patent implements periodic action through the use of pre-computed 3D Gaussian filter kernels that are applied systematically to photo-event data. The filter kernels are structured with periodic spatial sampling patterns that enable efficient convolution operations, allowing real-time coordinate transformation while managing algorithmic complexity through repeated, standardized processing patterns
Solution Approach 2:
The patent applies preliminary action by pre-computing and storing 3D Gaussian filter kernels and their corresponding weight lists before actual image formation. This preliminary preparation of filtering parameters enables faster real-time processing of photo-event data, as the complex Gaussian convolution operations can be executed using pre-prepared kernels rather than computing them on-the-fly
3Measurement precision
If blocking loss compensation is applied to correct detector saturation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by introducing blocking loss compensation factors that modify the weight assigned to photo-events based on their temporal characteristics. By changing the weighting parameters dynamically according to detected blocking conditions, the system corrects range profile accuracy without requiring complex structural modifications, simply adjusting computational parameters to compensate for detector saturation effects
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 enables real-time image formation and correction for saturated detector responses, enhancing the signal-to-noise ratio and reducing distortion in range profiles, suitable for applications like air-to-ground scanning and ground-to-air range-finding.
Implementation Method 1
A Geiger-mode avalanche photodiode (GMAPD) array is positioned to receive photons from portions of the laser light reflected off the at least one object
Implementation Method 2
A laser is configured to pulse laser light illuminating at least one object
Data Source
AI summary
A measured photo-event array is converted from two spatial coordinates and one temporal coordinate into three spatial coordinates for real-time imaging. Laser light pulses illuminate at least one object, and a Geiger-mode avalanche photodiode array receives photons from laser light reflected off the object. For each pulse of the laser light, the GMAPD outputs a first array of photo-events representative of reflected photons. A three-dimensional (3D) Gaussian distribution kernel arranged as a list of array locations to be processed and weight list(s) are provided. The weight list(s) specify an amount array values are scaled based on the Gaussian distribution or photon arrival time. A graphics processing unit arranges the first array of measured photo-events as a list, convolves the Gaussian displacement list with the list of measured photo-events to produce a convolution output, and applies weights from the weight list(s) to the values to produce a density point cloud.