3D LiDAR Motion Compensation for Multi-Dwell Target Imaging
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Solution Overview
Problem
Existing LADAR systems struggle to generate high-resolution images of targets with complex motion due to in-scene motion between dwells, leading to smearing and loss of non-static content in merged images, and motion compensation techniques are limited to coherent sensing over single contiguous dwells.
Innovation Solution
Combining multi-dwell imaging with motion compensation, the system extracts motion-compensated point clouds from each dwell, corrects for translation and orientation errors, and registers these point clouds to generate higher-quality multi-look imagery using state space carving and iterative closest point algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multi-dwell imaging is used to capture targets, then imaging coverage and data quantity are improved, but in-scene motion between dwells causes smearing and loss of non-static content
Solution Approach 1:
The system performs motion compensation by predicting target motion between dwells and pre-aligning the point clouds before merging. This preliminary action of motion prediction and compensation prevents the smearing effect that would otherwise occur during the multi-dwell imaging process, allowing high-resolution images of moving targets to be captured without degradation.
2Reliability
If motion compensation techniques are applied, then signal to noise ratio and target detection are improved, but the techniques are limited to single contiguous dwells and coherent sensing
Solution Approach 1:
The system implements a universal motion compensation algorithm that works with both coherent and direct-detect LADAR systems, and with both single-dwell and multi-dwell imaging modes. The algorithm uses state-space modeling and prediction that is independent of the specific sensing mode, making it adaptable to various LADAR configurations and operational requirements while maintaining improved target detection.
3Quantity of substance
If multiple dwells are merged to create final image, then data quantity and coverage are improved, but non-static content is smeared and lost
Solution Approach 1:
The system employs feedback through iterative optimization where the merged point clouds from multiple dwells are repeatedly processed to minimize motion-induced misalignment. The algorithm adjusts motion compensation parameters based on the quality of the merged result, using feedback from each iteration to improve the next, thereby preserving motion information while benefiting from increased data quantity.
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 produces higher-quality volumetric images by correcting for motion-related errors, enhancing image clarity and accuracy for targets with complex motion.
Implementation Method 1
pulsed LADAR systems provide an active sensing system that can determine the range to a target by measuring the time of flight (ToF) of short laser pulses reflected off the target
Implementation Method 2
A laser is one example of a light source that can be used in a LADAR/LiDAR system. Using a narrow laser beam, for example, a LADAR/LiDAR system can detect physical features of objects with extremely high resolutions
Data Source
AI summary
A plurality of scattered laser pulses is received, each scattered laser pulse associated with at least one plurality of respective dwells. A set of 3D velocity information is received, which is derived from photo events detected in information associated with the received plurality of scattered laser pulses. Each dwell in the plurality of dwells is associated with one or more photo events. The 3D velocity information includes information estimating each respective photo event's respective position in 6D space during the respective dwell associated with the photo event. For each dwell, its respective photo events are projected into a common reference frame, determined based on the 3D velocity information, to generate a set of motion-compensated point clouds. Each respective motion-compensated point cloud, for each dwell, is registered to the other motion-compensated point clouds in the set, to generate a set of registered point clouds, which are merged into a volumetric image.