Robot Multi-View Camera Recalibration Using Reprojection Error
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Solution Overview
Problem
The calibration of multi-view camera pairs in robots degrades over time due to vibrations, mechanical shocks, and environmental changes, affecting the robot's depth perception and interaction with its environment.
Innovation Solution
A system that monitors the reprojection error between cameras of a multi-view camera pair, initiating manual or automatic recalibration when the error exceeds a threshold, using machine-learning algorithms to update calibration parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is performed frequently to maintain accuracy, then calibration precision is improved, but loss of time and operational efficiency deteriorate
Solution Approach 1:
The system continuously monitors reprojection error between cameras and provides feedback when calibration accuracy degrades below a threshold, triggering automatic recalibration only when necessary. This feedback mechanism prevents unnecessary frequent calibrations while maintaining accuracy, resolving the contradiction between calibration precision and time loss.
Solution Approach 2:
The calibration system performs self-calibration automatically using machine learning algorithms when reprojection error exceeds thresholds, without requiring manual intervention. This self-service capability eliminates time loss associated with manual recalibration while maintaining calibration accuracy through automated monitoring and correction.
2Ease of operation
If automatic calibration is implemented to reduce manual intervention, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system replaces manual mechanical calibration operations with automated machine learning-based calibration algorithms. This substitution improves ease of operation by eliminating manual intervention while the software-based approach keeps hardware complexity minimal, effectively managing the trade-off between ease of operation and device complexity.
Solution Approach 2:
The system uses machine learning to dynamically adjust calibration parameters based on monitored reprojection errors and environmental conditions. This parameter adaptation enables automatic calibration operation while keeping the underlying hardware simple, resolving the contradiction between ease of operation and device complexity.
3Reliability
If continuous monitoring of reprojection error is performed to detect calibration degradation early, then reliability is improved, but use of energy increases
Solution Approach 1:
The system performs periodic monitoring of reprojection error at scheduled intervals rather than continuous monitoring, triggering calibration only when error thresholds are exceeded. This periodic approach maintains calibration reliability by detecting degradation events while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The monitoring system focuses computational resources on detecting specific calibration degradation events rather than continuously processing all camera data. This localized quality approach maintains reliability for detecting calibration issues while minimizing energy expenditure by activating full analysis only when necessary.
Data Source
AI summary
Systems and methods described herein relate to initiating calibration of a multi-view camera pair deployed in a robot. In one embodiment, a calibration initiation system in the robot monitors the reprojection error between cameras of the multi-view camera pair, the reprojection error measuring the calibration accuracy of the multi-view camera pair. The system also performs, when the reprojection error exceeds a predetermined threshold, one of (1) notifying a user that manual calibration of the multi-view camera pair is recommended and (2) initiating automatic calibration of the multi-view camera pair.