Autonomous Robot Image Processing with Calibration Plate Color Correction
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Solution Overview
Problem
Image processing systems for autonomous robots face challenges in accurately recognizing commodities due to color variations caused by camera position and lighting differences, leading to errors in self-positioning and commodity detection.
Innovation Solution
An image processing system that includes a calibration plate with color blocks to calculate a color correction matrix, corrects image colors using this matrix, and performs commodity recognition on the corrected images to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot uses pre-registered commodity colors for recognition, then the recognition process is simple and fast, but color differences due to camera position and lighting cause recognition accuracy to deteriorate
Solution Approach 1:
The system performs preliminary color correction by calculating a color correction matrix from a calibration plate before commodity recognition. This pre-processing step adjusts the captured image colors to match the pre-registered commodity colors, ensuring that subsequent recognition operations achieve high accuracy without requiring complex real-time color analysis
Solution Approach 2:
The calibration plate serves as an intermediary element between the camera and the commodity. It provides known reference colors that mediate the color transformation process, allowing the system to compute correction values that bridge the gap between captured image colors and pre-registered commodity colors
2Extent of automation
If the robot captures images at predetermined positions based on environment map, then self-positioning can be maintained, but positioning errors cause the captured images to differ from expected views
Solution Approach 1:
The system applies color correction as a cushioning measure against positioning errors. By pre-calculating color correction values based on the robot's intended position and orientation, the system compensates for the expected color variations that would result from positioning inaccuracies, ensuring consistent commodity recognition even when the robot stops at slightly different locations
3Measurement precision
If the system applies color correction processing, then commodity recognition accuracy improves, but processing time and computational load increase
Solution Approach 1:
The color correction matrix is calculated in advance from the calibration plate data before actual commodity recognition begins. This preliminary computation allows the system to apply simple matrix multiplication during recognition operations, significantly reducing the processing time required during actual commodity detection while maintaining high accuracy
Solution Approach 2:
The system transforms the color correction problem from a complex pixel-by-pixel adjustment into a simplified parameter transformation using a pre-calculated correction matrix. This parameter-based approach changes the correction process from intensive image processing to efficient linear algebra operations, reducing computational load and processing time
Data Source
AI summary
An autonomous inventory tracking apparatus includes an image acquisition unit configured to acquire an image, and a processor configured to detect a calibration plate in the image acquired from the image acquisition unit, calculate a color correction value for the image according to a color block of the calibration plate, the color block matching a reference value, correct color in the image using the calculated color correction value to provide a color-corrected image, and perform commodity recognition processing on the color-corrected image so as to identify the commodity in the image acquired from the image acquisition unit.