Autocalibration for Multi-Camera Systems Using Natural Features
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Solution Overview
Problem
Current calibration methods for multi-camera and multisensory systems are inadequate, often requiring specialized calibration patterns, lacking autocalibration capabilities, and resulting in inaccurate measurements due to insufficient precision in estimating intrinsic and extrinsic parameters.
Innovation Solution
The development of new techniques for in-line and offline calibration, including multiple variants of calibration targets and cost functions that utilize constancy relationships of objects in the field, reduce computational complexity and improve accuracy. These methods are scalable across various camera configurations and focal lengths, and can be extended to systems combining cameras with LIDAR and radar.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized calibration patterns are used for calibration, then calibration accuracy can be improved, but device complexity and ease of operation deteriorate due to requiring specialized targets
Solution Approach 1:
The system performs autocalibration using naturally occurring features in the environment without requiring specialized calibration patterns or targets. The calibration process is self-service in that the system automatically identifies and uses available visual features (edges, corners, lines) from the operating environment to calibrate its own camera and sensor parameters.
Solution Approach 2:
The invention extracts calibration information from naturally occurring features in the environment rather than requiring external specialized calibration targets. It separates the calibration function from dedicated calibration equipment and integrates it into the normal operating environment using available visual features.
2Extent of automation
If traditional calibration algorithms are used, then some calibration parameters can be estimated, but measurement precision deteriorates due to insufficient accuracy in estimating intrinsic and extrinsic parameters
Solution Approach 1:
The invention combines multiple calibration approaches (structure-from-motion, feature detection, constraint optimization) into a unified autocalibration system. It merges intrinsic parameter calibration with extrinsic parameter calibration and integrates them with normal operational data processing to achieve high precision without specialized targets.
Solution Approach 2:
The system uses iterative optimization with feedback loops that continuously refine parameter estimates by comparing predicted feature locations with actual observations. The calibration parameters are refined through multiple passes using constraint satisfaction and error minimization based on observed feature correspondences across multiple views.
3Ease of operation
If calibration is performed without known three-dimensional information, then ease of operation improves, but measurement precision deteriorates due to unknown scale
Solution Approach 1:
The system changes the parameter representation by working with projective geometry and homogeneous coordinates, allowing calibration up to scale initially, then resolving scale through constraints from multiple views and temporal consistency. It transforms the calibration problem from requiring absolute scale to achieving scale through relative measurements and constraints.
Data Source
AI summary
A method and apparatus for calibrating an image capture device are provided. The method includes capturing one or more of a single or Multiview image set by the image capture device, detecting one or more calibration features in each set by a processor, initializing each of the one or more calibration parameters a corresponding default value, extracting one or more relevant calibration parameters, computing an individual cost term for each of the identified relevant calibration parameters, and scaling each of the relevant cost terms. The method continues with combining all the cost terms once each of the calculated relevant cost terms have been scaled, determining if the combination of the cost terms has been minimized, adjusting the calibration parameters if it is determined that that the combination of the cost terms has not been minimized, and returning to the step of extracting one or more of the relevant calibration parameters.