Change Invariant Scene Recognition by Agent
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Solution Overview
Problem
Current automated agents face challenges in localization and navigation due to the high cost of high-precision sensors and the imperfections of image-based systems, particularly with perceptual aliasing and illumination changes, which affect the accuracy of scene recognition and navigation.
Innovation Solution
The technology employs image sensors to capture images and groups them into situation image sets based on temporal and environmental conditions, allowing for change-invariant scene recognition by calculating probabilities for matching navigation images, reducing the need for exact matches and improving navigation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-precision laser sensors are used for agent localization and navigation, then measurement precision and reliability are improved, but device cost increases significantly
Solution Approach 1:
The patent replaces expensive high-precision laser sensors with cheaper camera-based image sensors that capture visual images of the environment. The system creates a topological map by comparing captured images with pre-stored reference images, effectively copying the localization function from expensive sensor hardware to affordable image processing software algorithms.
Solution Approach 2:
The patent uses inexpensive camera modules instead of costly laser ranging sensors. While cameras provide less direct metric information, the system compensates through computational methods, making the overall localization system more cost-effective for resource-constrained agents.
2Ease of manufacture
If image sensors are used for scene recognition, then device cost is reduced, but measurement precision and reliability deteriorate due to perceptual aliasing and illumination changes
Solution Approach 1:
The patent divides the image recognition process into multiple stages: first creating a topological map by comparing images with reference images to identify location, then using this location information to guide navigation decisions. This segmentation allows the system to achieve reliable localization without requiring every image comparison to be perfectly accurate.
Solution Approach 2:
The system dynamically adjusts its matching criteria based on the application context. For navigation purposes, it uses topological matching that is tolerant of illumination changes and minor scene variations, focusing on identifying sufficient location correspondence rather than requiring pixel-perfect accuracy.
3Device complexity
If traditional image matching is used for navigation, then simplicity is maintained, but navigation reliability deteriorates under illumination changes and scene variations
Solution Approach 1:
The patent performs preliminary image processing by comparing captured images with pre-stored reference images before navigation decisions are made. This preliminary topological mapping step creates a location context that makes subsequent navigation more reliable, as the system knows which reference images correspond to which locations in advance.
Solution Approach 2:
The patent introduces a topological map as an intermediary data structure between the image sensor and the navigation controller. This topological representation abstracts away the details of illumination changes and scene variations, providing a stable intermediate representation that improves navigation reliability without significantly increasing system complexity.
4Measurement precision
If exact image matching is required for localization, then measurement precision is improved, but processing time increases significantly
Solution Approach 1:
The patent uses partial matching criteria for topological localization instead of requiring complete image correspondence. The system identifies locations based on sufficient similarity between captured and reference images, accepting approximate matches that are adequate for navigation purposes. This partial action approach reduces processing time while maintaining sufficient accuracy for the application.
Solution Approach 2:
The system changes the matching parameters from strict pixel-level comparison to more tolerant topological feature comparison. By adjusting the matching criteria to focus on key structural features rather than exact pixel values, the system achieves faster matching speeds while maintaining reliable location identification for navigation.
Data Source
AI summary
A technology can be provided for change invariant scene recognition of an environment by an agent that uses image sensors to capture images. An example method can include grouping the images into situation image sets to form an image database. A navigation image can be captured by the agent at the agent's current location. Another operation can be searching through images by separately loaded situation image set to identify images that are a probable match for the navigation image. Error values can be computed between the navigation image and situation image sets. A probability can be calculated that situation image sets are to be used as a relevant situation image set based on the error values. Then the relevant situation image set can be searched for matching purposes with the navigation image while the error value is below a defined threshold.