Robot Camera Calibration Using ROI-Guided Pose Sampling
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Solution Overview
Problem
The existing camera calibration process for robotic systems is cumbersome, requires fiducial markers, and is not scalable for different product lines and work zones, posing safety challenges and uncertainty in accuracy.
Innovation Solution
An automated camera calibration system that allows for remote, 'button click' calibration of cameras using a central service, which includes a graphical user interface for selecting robots and cameras, and performs calibration based on specific robotic applications and workspace attributes, utilizing AprilTag fiducial markers and ray-based calibration methods to generate transformation matrices for accurate 3D data alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration with fiducial markers is used, then calibration accuracy can be achieved, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system pre-generates a optimized sequence of robot poses and camera triggers before calibration execution. The pose sequence is calculated in advance based on the workspace geometry and camera positions, eliminating the need for manual pose selection during calibration. This preliminary preparation significantly reduces calibration time while maintaining accuracy.
Solution Approach 2:
The calibration system is fully automated and self-executing. Once configured with robot and camera parameters, the system autonomously moves the robot through predetermined poses, triggers camera captures, and processes calibration data without requiring manual operation. The robot serves itself by automatically positioning fiducial markers in view of the camera according to the pre-planned sequence.
2Ease of manufacture
If fixed joint angles are used for data collection, then calibration can be performed, but safety challenges arise requiring ambient space to be clear of obstacles
Solution Approach 1:
The calibration system uses dynamic, adaptive pose selection rather than fixed joint angles. The pose sequence is generated based on workspace geometry and can be adjusted to avoid obstacles and safety zones. The system dynamically determines safe operating positions by considering the robot's workspace boundaries and any defined exclusion zones, allowing calibration to proceed without requiring complete clearance of the ambient space.
3Measurement precision
If traditional calibration methods are used, then single workspace calibration is achieved, but scalability to different product lines and work zones is limited
Solution Approach 1:
The calibration system is designed to be universally applicable across multiple product lines and workspace configurations. It supports calibration of multiple cameras simultaneously and can handle different robot types, end-effectors, and workspace geometries. The system automatically adapts to different scenarios by receiving configuration parameters specific to each application, making it scalable without requiring method changes for different product lines.
Solution Approach 2:
The calibration process is segmented into independent, configurable components: robot configuration, camera configuration, workspace definition, and calibration execution. Each component can be independently configured and modified. The pose sequence generation is divided into discrete poses that can be selectively executed, allowing the system to adapt to different workspace sizes and requirements while maintaining the same overall calibration methodology.
4Measurement precision
If work zone variability is accommodated, then calibration accuracy for specific workspaces improves, but the complexity of evaluating accuracy across different regions increases
Solution Approach 1:
The system generates pose sequences tailored to specific regions of interest within the workspace. Different workspace zones can be defined with different priority levels or accuracy requirements, and the pose generation algorithm adjusts accordingly to provide higher density sampling in critical areas. This local optimization allows accurate calibration for specific work zones without requiring uniform coverage of the entire workspace.
Solution Approach 2:
The system includes automated accuracy evaluation that provides feedback on calibration quality across different workspace regions. After calibration, the system can evaluate results at multiple test poses and provide quantitative accuracy metrics for different zones. This automated feedback eliminates the need for manual accuracy assessment and simplifies the evaluation process while providing detailed information about workspace-specific calibration quality.
Data Source
AI summary
Techniques are disclosed to calibrate a camera for use with one or more robots to perform a robotic application. In various embodiments, selection of a camera to be calibrated is received via a user interface. A region of interest associated with the camera and a robot with which the camera is associated is determined. A set of sample points within the region of interest is selected. The robot is moved through a set of trajectories to position the robot, successively with respect to each of at least a subset of the sample points, in a predetermined pose at a location associated with the sample point and, at each location cause the camera to generate a corresponding image that includes at least a fiducial marker located on the robot. The respective predetermined poses and corresponding images are used to perform a set of calibration computations with respect to the camera.