Camera Pose Recognition Using Simulated Distinctive Features
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Solution Overview
Problem
Existing methods for recognizing the position and attitude of an object imaged by a camera require manual user input to distinguish similar attitudes, which is time-consuming and prone to human error.
Innovation Solution
A method and system that utilize distinctive features extracted from a simulation model to automatically distinguish between similar attitudes of an object, using a camera to capture an image, estimate the position and attitude, and determine the correct attitude based on these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual settings are used to distinguish similar attitudes, then the system can recognize position and attitude, but time and effort are required and human error may occur
Solution Approach 1:
The system performs self-service by automatically extracting distinctive features from the object image and determining attitude without requiring manual user input. The feature extraction unit and attitude determination unit work autonomously to distinguish similar attitudes, eliminating the need for users to manually designate distinguishing portions.
Solution Approach 2:
The manual mechanical process of designating portions via user interface is replaced with an automated image processing system. The system uses simulation models and feature extraction algorithms to automatically distinguish attitudes, substituting human manual operations with computational processes.
2Ease of operation
If manual settings are used to distinguish similar attitudes, then the system can recognize position and attitude, but wrong settings may be made by human error
Solution Approach 1:
The system performs self-service by automatically extracting distinctive features from the object image and determining attitude without requiring manual user input. The feature extraction unit and attitude determination unit work autonomously to distinguish similar attitudes, eliminating the need for users to manually designate distinguishing portions.
Solution Approach 2:
The system uses feedback from the simulation model comparison to automatically adjust and determine the correct attitude. By comparing extracted features against simulated attitudes and using feedback from this comparison, the system reliably determines the object's attitude without manual intervention.
3Productivity
If automatic recognition is implemented, then time consumption is reduced, but complex processing is required to distinguish similar attitudes
Solution Approach 1:
The complex recognition process is segmented into distinct functional units: a feature extraction unit that identifies distinctive features, a simulation model unit that generates reference attitudes, and an attitude determination unit that compares and determines the final attitude. This segmentation manages complexity by dividing the overall task into specialized sub-tasks.
Solution Approach 2:
The system performs preliminary action by pre-computing simulation models of the object in various attitudes before actual recognition occurs. These pre-computed models serve as reference data that speed up the actual attitude determination process, allowing for rapid comparison and recognition.
Data Source
AI summary
A method of the present disclosure includes (a) extracting distinctive features used for respectively distinguishing a plurality of similar attitudes from which images similar to one another are obtained using a simulation model of an object, (b) capturing an object image of the object using a camera, (c) estimating a position and an attitude of the object using the object image, and (d) when the estimated attitude corresponds to one of the plurality of similar attitudes, determining the one of the plurality of similar attitudes as the attitude of the object using the distinctive features.