Visual Odometry for Optical Pattern Scanning
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Solution Overview
Problem
Mobile devices with cameras face challenges in quickly and efficiently scanning multiple optical patterns, such as barcodes, due to limitations in processing power and accuracy.
Innovation Solution
The implementation of a method using visual odometry and image analysis to detect and decode optical patterns, which involves tracking camera movement, calculating transformations between image frames, and segmenting images to enhance scanning efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If visual odometry and image analysis are used to detect and decode optical patterns, then scanning speed and accuracy are improved, but processing complexity increases
Solution Approach 1:
The patent segments the image processing task into distinct stages: feature detection, visual odometry calculation, pattern recognition, and decoding. By dividing the complex processing into modular segments, the system achieves high scanning speed through parallel processing while managing complexity through organized task separation.
Solution Approach 2:
The patent performs preliminary feature detection and visual odometry calculations before actual pattern decoding. By pre-processing the image data to identify key features and camera motion characteristics in advance, the system reduces the computational burden during the decoding phase, thereby improving overall scanning speed while maintaining manageable processing complexity.
2Measurement precision
If real-time tracking of camera movement is implemented, then scanning accuracy is improved, but computational load increases
Solution Approach 1:
The patent replaces complex mechanical motion tracking systems with visual odometry based on image analysis. By using 2D image features and homography transformations instead of sophisticated 3D motion sensors, the system achieves accurate real-time camera position tracking while significantly reducing computational load and energy consumption.
Solution Approach 2:
The patent changes the parameter space for motion tracking from 3D spatial coordinates to 2D image plane coordinates through homography transformations. This parameter transformation simplifies the computational requirements for real-time tracking while maintaining sufficient accuracy for barcode scanning applications.
3Measurement precision
If multiple images are acquired and processed, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements continuous image acquisition and processing where multiple images are captured in rapid succession and processed in an overlapping pipeline. While one image is being decoded, the next image is already being pre-processed, ensuring continuous useful action that improves detection accuracy through multiple samples while minimizing overall processing time.
Solution Approach 2:
The patent implements selective image processing where not all captured images are fully decoded. The system quickly evaluates image quality metrics and skips processing for images that are out of focus, too blurry, or clearly do not contain patterns, thereby maintaining high detection accuracy for valid images while reducing total processing time by eliminating wasted computation.
Data Source
AI summary
Visual odometry is used for tracking an optical pattern outside a preview area of a camera. The optical pattern is detected in a first image, while the camera is at a first position. A second image is acquired while the camera is at a second position. A transformation is calculated that relates the first position to the second position. A location of the optical pattern in relation to the second image is calculated based on the transformation. Calculation of the transformation can be simplified by assuming that the camera is moved in a plane parallel to a plane having multiple optical patterns.