Neural Network Camera-to-Robot Pose Estimation Without Calibration
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Solution Overview
Problem
Determining the position and orientation of a camera relative to a robot for accurate navigation and interaction is cumbersome and requires a lengthy calibration process.
Innovation Solution
A neural network is trained to analyze captured images of a robot to identify specific features, generating belief maps that are used in conjunction with camera intrinsics and robot kinematics to determine the camera-to-robot pose, eliminating the need for extensive calibration libraries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used to determine camera position and orientation, then measurement accuracy is improved, but calibration time and process complexity increase significantly
Solution Approach 1:
The patent replaces traditional mechanical calibration procedures with a neural network-based computational system. The neural network automatically learns camera pose from images by analyzing visual features and spatial relationships, eliminating the need for manual calibration processes while achieving accurate pose determination.
Solution Approach 2:
The patent creates a virtual representation of the calibration process through neural network training. During training, the network learns from synthetic or real image data what camera poses correspond to specific visual patterns, effectively copying the complex calibration relationships into the network's parameter space for rapid inference.
2Measurement precision
If traditional calibration methods are used to determine camera position and orientation, then measurement accuracy is improved, but device complexity and setup requirements increase
Solution Approach 1:
The patent replaces complex mechanical calibration hardware and procedures with a software-based neural network system. The network processes images directly to determine pose, eliminating the need for calibration libraries, fiducial markers, and manual alignment procedures while maintaining measurement accuracy.
Solution Approach 2:
The neural network model serves multiple functions: it performs pose estimation, recognizes visual features, and infers spatial relationships simultaneously. This multi-functionality consolidates what would traditionally require separate calibration components and procedures into a single integrated system.
3Measurement precision
If extensive calibration libraries and fiducials are used, then pose determination accuracy is improved, but the system becomes less adaptable to unstructured environments
Solution Approach 1:
The neural network performs self-calibration by automatically learning the relationship between camera views and robot pose from training data. The system serves its own calibration needs without requiring external fiducial markers or pre-configured calibration libraries, enabling it to adapt to various environments including unstructured ones.
Solution Approach 2:
The patent changes the approach from fixed calibration parameters (fiducial marker positions, camera intrinsics) to learned parameters in the neural network. The network's parameters are adjusted during training to capture pose-image relationships, allowing the system to adapt to different environments without requiring changes to physical calibration hardware.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to determine a pose of an object. In at least one embodiment, a network is trained to predict a pose of an autonomous object based, at least in part, on only one image of the autonomous object.