Vehicle Camera Calibration for Extrinsics and Time Offset
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Solution Overview
Problem
Current methods for calibrating cameras on vehicles, especially those not present during factory calibration or with unsynchronized clocks, lack accuracy and efficiency in determining spatial and temporal alignment with other sensors.
Innovation Solution
A method involving the collection of image data from multiple cameras, where optimization algorithms determine a candidate extrinsics transformation and time offset to minimize reprojection errors based on shared landmarks, allowing for accurate calibration of camera systems on vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If factory calibration methods are used for cameras not present during calibration, then calibration can be performed, but accuracy deteriorates due to unsynchronized clocks and spatial misalignment
Solution Approach 1:
The system performs self-calibration by using its own captured image data and shared landmarks to automatically determine extrinsics transformation and time offset, eliminating the need for external factory calibration equipment and synchronized clocks
Solution Approach 2:
The optimization process uses reprojection error feedback to iteratively adjust extrinsics transformation and time offset parameters, minimizing the difference between observed and predicted landmark positions until convergence is achieved
2Measurement precision
If optimization algorithms are applied to minimize reprojection error, then calibration precision improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by capturing multiple images during a calibration period and pre-identifying shared landmarks across images before running optimization, preparing data structures and initial estimates to reduce computational burden during the actual optimization process
Solution Approach 2:
The calibration process is segmented into distinct phases: image collection, landmark identification, initial parameter estimation, and optimization refinement, allowing computational tasks to be distributed and managed more efficiently
Data Source
AI summary
Examples disclosed herein involve a computing system configured to (i) obtain first image data captured by a first camera of a vehicle during a given period of operation of the vehicle, (ii) obtain second image data captured by a second camera of the vehicle during the given period of operation, (iii) based on the obtained first and second image data, determine (a) a candidate extrinsics transformation between the first camera and the second camera and (b) a candidate time offset between the first camera and the second camera, and (iv) based on (a) the candidate extrinsics transformation and (b) the candidate time offset, apply optimization to determine a combination of (a) an extrinsics transformation and (b) a time offset that minimizes a reprojection error in the first image data, where the reprojection error is defined based on a representation of at least one landmark that is included in both the first and second image data.