Edge Detection via Window Grid Normalization for INS Correction
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Solution Overview
Problem
Inertial Navigation Systems (INS) using Inertial Measurement Units (IMU) suffer from accumulated error, leading to navigation parameter drift, and GPS corrections are unreliable due to geographic outages and signal interference, necessitating a method for correcting navigation parameters without GPS availability.
Innovation Solution
The method involves generating and comparing edge maps from remote sensing images and vehicle-acquired images, adjusting edge detection algorithm parameters based on spatial resolutions, and using these edge maps to correct navigation data, allowing for precise vehicle positioning even without GPS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used to correct navigation parameters, then navigation accuracy is improved, but reliability deteriorates due to geographic outages and signal interference
Solution Approach 1:
The patent changes the correction source from GPS signals to terrain elevation data from multiple sources (satellite imagery, aerial photography, LiDAR). By transforming the correction mechanism from signal-based to data-based, the system achieves both high accuracy and reliability without geographic outages or jamming vulnerabilities
Solution Approach 2:
The patent introduces an intermediary correction system that uses pre-acquired terrain elevation data as a mediator between the IMU and the navigation solution. This intermediary layer provides continuous correction without direct dependency on GPS signals, resolving the contradiction between accuracy and reliability
2Measurement precision
If window grid normalization is used for edge detection, then edge detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the image processing into distinct stages: acquiring images at multiple resolutions, detecting edges at each resolution level, and integrating results from coarse to fine scales. This segmentation allows accurate edge detection while managing computational complexity through hierarchical processing
Solution Approach 2:
The patent performs edge detection at multiple resolution levels beyond what a single-scale detector would provide. By detecting edges at coarse resolutions first and then refining at finer resolutions, the system achieves superior accuracy while distributing computational load across multiple passes rather than one excessive computation
Data Source
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AI summary
In an embodiment, a method is provided. The method comprises extracting, from an image acquired with a first image-capture device, an image portion having dimensions of an extent of a second image-capture device. The method further comprises normalizing a parameter of the image portion. The method further comprises determining at least one edge of at least one object in the image portion; and generating, in response to the determined at least one edge, an edge map corresponding to the image portion.