3D Camera Positioning via Self-Calibrating Feature Tracking
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Solution Overview
Problem
Existing imaging systems face challenges in accurately determining and updating coordinate conversion parameters, which are essential for 3D positioning of cameras, especially when the original parameters are unavailable or become inaccurate over time, leading to mismatched images and target feature points.
Innovation Solution
A method and system that involve capturing initial and subsequent images of a feature point, updating the initial coordinate conversion parameters based on the coordinates of the feature point in both images, allowing the camera to adjust its orientation and generating an updated coordinate conversion parameter set for precise 3D positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manufacturer-provided coordinate conversion parameters are used, then 3D positioning can be performed, but the positioning accuracy deteriorates when parameters are unavailable or become inaccurate over time
Solution Approach 1:
The system performs self-calibration by automatically capturing images at multiple orientations, extracting feature points, and computing updated coordinate conversion parameters without requiring external intervention or manufacturer-provided parameters. This self-service mechanism ensures continuous availability and accuracy of positioning parameters.
Solution Approach 2:
The system uses feedback from captured images and extracted feature points to iteratively refine and update the coordinate conversion parameters. By comparing observed feature point positions with expected positions based on current parameters, the system adjusts parameters to improve positioning accuracy over time.
2Measurement precision
If coordinate conversion parameters are updated frequently, then positioning accuracy is maintained, but system complexity and processing time increase
Solution Approach 1:
The system performs coordinate conversion parameter updates periodically or on-demand rather than continuously, capturing images at predetermined intervals or when triggered by specific events. This periodic approach maintains positioning accuracy while avoiding excessive processing time and system complexity.
3Measurement precision
If manual calibration is performed to obtain accurate parameters, then positioning precision improves, but ease of operation deteriorates
Solution Approach 1:
The system automatically performs the entire calibration process including image capture, feature point extraction, and parameter computation without requiring manual intervention. This self-service approach maintains high positioning precision while significantly improving operational convenience.
Solution Approach 2:
The system replaces manual mechanical calibration operations with automated computational methods. Instead of physically adjusting and manually measuring parameters, the system uses image processing algorithms and coordinate transformations to automatically determine accurate parameter values.
Data Source
AI summary
A method and a system for updating coordinate conversion parameters are provided. The method may include obtaining a first image captured by an imaging apparatus under a first configuration, selecting a feature point of an object in the first image and obtaining first coordinates of the feature point of the object in the first image. The method may further include causing the imaging apparatus to change from the first configuration to a second configuration. The method may further include obtaining a second image captured by the imaging apparatus under the second configuration, identifying the feature point of the object in the second image, and obtaining second coordinates of the feature point of the object in the second image. The method may further include updating the initial coordinate conversion parameters to generate an updated coordinate conversion parameter set.