Vehicle Camera Calibration for Extrinsics and Time Offset Alignment
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Solution Overview
Problem
Current methods for calibrating cameras on vehicles are inefficient and lack accuracy, particularly in determining extrinsics and time offset between cameras, which hinders the alignment of image data with other sensor data, impacting the usability of sensor data for tasks like autonomous driving and map creation.
Innovation Solution
A method involving the collection of timestamped image data from multiple cameras to determine an extrinsics transformation and time offset using optimization algorithms, minimizing reprojection errors, thereby enabling accurate spatial and temporal alignment of image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used, then the calibration process is simpler, but the accuracy of extrinsics and time offset determination deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting multiple images from both cameras before calibration, capturing temporal relationships and motion data in advance. This preliminary data collection enables more accurate extrinsics and time offset determination through subsequent optimization analysis.
Solution Approach 2:
The calibration method transitions from traditional 2D image plane analysis to 3D spatial reconstruction by determining camera poses and extrinsics in three-dimensional space. This dimensional expansion enables more accurate calibration by utilizing depth information and spatial relationships beyond the image plane.
2Measurement precision
If calibration accuracy is improved through optimization, then sensor data alignment quality improves, but computational time and resources increase
Solution Approach 1:
Multiple images are collected in advance during a calibration period, pre-capturing the temporal and spatial relationships needed for optimization. This preliminary data gathering reduces the computational burden during the actual calibration process by having ready-to-analyze image sets.
Solution Approach 2:
The calibration process continuously refines extrinsics and time offset parameters through optimization iterations, maintaining continuous improvement of alignment accuracy. The system performs ongoing optimization until convergence, ensuring the best possible calibration results.
3Measurement precision
If multiple images are collected for calibration, then calibration accuracy improves, but data processing complexity increases
Solution Approach 1:
The calibration process segments the analysis by first determining poses for the second camera across multiple images, then using these poses to calculate extrinsics and time offset. This segmentation of the processing pipeline manages complexity by breaking down the overall calibration task into manageable stages.
Solution Approach 2:
Camera poses serve as an intermediary representation between the raw image data and the final calibration parameters. By introducing poses as an intermediate step, the system simplifies the relationship between multiple images and the extrinsics/time offset determination, making the processing more tractable.
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.