Surface Imaging Localization Using Log Gabor Templates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional autonomous devices face challenges in navigating and mapping complex environments due to high data bandwidth requirements, computational deficiencies, and fidelity issues with existing localization techniques, which hinder their ability to make accurate decisions.
Innovation Solution
The implementation of localization systems and methods using surface structural patterns, where a raw image of a target surface is encoded into a template using transforms like log Gabor feature encoders, allowing for efficient comparison with reference templates to determine the location, enabling accurate navigation and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SLAM techniques are used for localization and mapping, then the device can generate and update a map of the environment, but the data bandwidth requirements and computational resources become prohibitively high
Solution Approach 1:
The patent extracts only the essential surface structural pattern information from the full image data. Instead of processing complete images, the system identifies and processes only the unique surface patterns that are sufficient for localization, thereby reducing computational requirements while maintaining localization accuracy.
Solution Approach 2:
The patent segments the image processing task into distinct stages: capturing raw images, encoding them into compact templates using transforms, and then comparing these templates against a pre-stored gallery. This segmentation allows each stage to be optimized independently, reducing overall computational complexity.
2Measurement precision
If conventional SLAM techniques are used for localization and mapping, then the device can track its location in the environment, but the fidelity and quality of localization information are insufficient
Solution Approach 1:
The patent transforms the raw image data into a different parameter space using mathematical transforms. This parameter transformation allows the system to represent surface structural patterns in a form that is both compact and faithful to the original surface information, preserving fidelity while reducing data volume.
3Measurement precision
If high-resolution surface images are captured for accurate localization, then the localization precision improves, but the data bandwidth requirements increase significantly
Solution Approach 1:
The patent creates compact template copies of the surface patterns that retain the essential identifying features. These templates serve as simplified representations that can be stored and transmitted with minimal bandwidth while still enabling accurate matching and localization.
Solution Approach 2:
The patent performs preliminary encoding of surface patterns into templates before they need to be compared or transmitted. This advance processing reduces the data volume that needs to be handled during actual localization operations, thereby reducing bandwidth requirements during critical operations.
Data Source
AI summary
Implementations described and claimed herein provide localization systems and methods using surface imaging. In one implementation, a raw image of a target surface is captured using at least one imager. The raw image is encoded into a template using at least one transform. The template specifies a course direction and an intensity gradient at one or more spatial frequencies of a pattern of the target surface. The template is compared to a subset of reference templates selected from a gallery stored in one or more storage media. A location of the target surface is identified when the template matches a reference template in the subset.