Omni-directional Image Data Segmentation for Position Estimation
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Solution Overview
Problem
Existing methods for processing omni-directional image data are inefficient and require significant resources for storage and processing, especially as the resolution increases, making it challenging to quickly and compactly process this data for applications like robot position estimation.
Innovation Solution
The method involves segmenting omni-directional image data into multiple image slices, calculating a slice descriptor for each slice, and generating a sequence of these descriptors, which allows for efficient representation and comparison to estimate position or orientation, reducing processing and storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If omni-directional image data is processed at high resolution, then measurement precision is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent divides the omni-directional image into multiple overlapping sub-images or image strips, processes each segment independently to extract visual features, and then integrates these features. This segmentation approach reduces the computational complexity of processing the entire high-resolution omni-directional image at once, thereby decreasing processing time while maintaining position estimation precision through the integration of features from all segments.
2Measurement precision
If omni-directional image data is stored at high resolution, then measurement precision is improved, but storage requirements increase
Solution Approach 1:
The patent extracts only the essential visual features from the high-resolution omni-directional image data rather than storing the complete raw image. By identifying and retaining only the discriminative features necessary for position estimation, the system maintains measurement precision while significantly reducing the quantity of data that needs to be stored and processed.
3Loss of information
If traditional methods are used to process omni-directional image data, then completeness of information is maintained, but device complexity and processing resources increase
Solution Approach 1:
The patent performs preliminary processing of the omni-directional image data by dividing it into segments and extracting visual features before the main position estimation process. This preliminary action organizes the data into a more manageable format, reducing the complexity of subsequent processing steps while preserving all necessary information for accurate position estimation through proper feature integration.
Data Source
AI summary
Methods, systems, and robots for processing omni-directional image data are disclosed. A method includes receiving omni-directional image data representative of a panoramic field of view and segmenting, by one or more processors, the omni-directional image data into a plurality of image slices. Each image slice of the plurality of image slices is representative of at least a portion of the panoramic field of view of the omni-directional image data. The method further includes calculating a slice descriptor for each image slice of the plurality of image slices and generating a current sequence of slice descriptors. The current sequence of slice descriptors includes the calculated slice descriptor for each image slice of the plurality of image slices.