Camera-Radar Calibration for Real-Time Coordinate Fusion
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Solution Overview
Problem
Fusing radar data with image data for real-time applications is challenging due to physical variations in sensor behavior, requiring calibration to correlate data on a common basis and address lens distortions and device characteristics.
Innovation Solution
An apparatus and method for calibrating a single lens camera and radar, involving an interface to receive pixel and radar data, processing the data to generate intrinsic matrices and transformation matrices, and implementing coordinate transformations to align radar and image coordinates with environment coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If radar data and image data are fused for real-time applications, then rich data source and comprehensive information are achieved, but data processing complexity and computational load increase significantly
Solution Approach 1:
The patent segments the fusion process into distinct stages: calibration phase (establishing transformation matrices) and runtime phase (applying pre-computed mappings). This segmentation allows complex processing to be done once during calibration, simplifying real-time operations.
Solution Approach 2:
The patent performs preliminary calibration to compute transformation matrices and mapping functions between radar and image coordinate systems before actual fusion operations. This preliminary action stores the coordinate relationships, enabling efficient real-time fusion without repeated complex calculations.
2Measurement precision
If calibration is performed to correlate radar and image data on a common basis, then measurement precision and data correlation are improved, but device complexity and calibration process difficulty increase
Solution Approach 1:
The patent introduces an intermediary calibration process that establishes transformation matrices as a mediator between radar and image coordinate systems. This intermediary layer enables precise correlation without requiring direct complex interactions between the two different sensor systems.
Solution Approach 2:
The patent changes coordinate system parameters by computing transformation matrices that map radar coordinates to image coordinates. This parameter transformation allows data from different coordinate systems to be correlated precisely on a common basis.
3Manufacturing precision
If lens distortions and device characteristics are corrected through calibration, then manufacturing precision and data accuracy are improved, but ease of manufacture and system setup become more difficult
Solution Approach 1:
The patent implements self-service calibration where the system automatically computes transformation matrices and mapping functions without requiring manual intervention. The calibration process is performed autonomously, reducing the burden on manufacturers and simplifying system setup.
Solution Approach 2:
The patent replaces manual mechanical calibration procedures with automated computational methods. Instead of physical adjustments and manual alignment, the system uses algorithmic computation of transformation matrices to achieve precise calibration.
4Productivity
If large volumes of radar and image data are processed quickly for real-time applications, then processing speed is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary computation of transformation matrices and mapping functions during calibration, storing these for reuse. This eliminates the need to perform complex calculations during real-time processing, enabling fast processing of large data volumes.
Solution Approach 2:
The patent creates a copied coordinate mapping system where transformation relationships are pre-computed and stored. Instead of recalculating coordinate transformations for each data point, the system uses pre-established mapping functions, significantly reducing computational requirements during real-time operation.
Data Source
AI summary
An apparatus comprising an interface and a processor. The interface may receive pixel data and radar data. The processor may process the pixel data arranged as video frames, calculate an intrinsic matrix for an image sensor, generate image mapping points to an environment, generate an image transformation matrix in response to the mapping points, the intrinsic matrix and depth data, define a valid area in response to the radar data of an object, generate radar mapping points in response to an environment, generate a mapping function in response to the radar mapping points and a transfer operation, and implement a coordinate transformation in response to the image transformation matrix and the mapping function. The coordinate transformation may be configured to transfer radar coordinates to image coordinates in response to the mapping function and transfer the image coordinates to environment coordinates using the image transformation matrix.