Vehicle Camera Extrinsic Parameter Calibration via Neural Network
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Solution Overview
Problem
Existing methods for establishing extrinsic camera parameters in vehicles are costly, inaccurate, and require frequent recalibration due to external influences, with online calibration methods having limited applicability and relying on geometric assumptions or sensor data.
Innovation Solution
A method that uses a combination of reference sensor technology during an initial measurement run to establish precise extrinsic parameters, which are then stored and used to train a convolutional neural network for online recalibration, allowing for continuous, accurate determination of camera positions and angles without the need for ongoing reference sensor use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration procedures are used at the end of production, then initial camera parameters can be established, but the calibration costs are high and the accuracy deteriorates over time due to external influences
Solution Approach 1:
The system performs preliminary calibration during the production phase to establish initial extrinsic parameters, then uses online calibration methods during operation to maintain accuracy without requiring repeated factory calibrations. This preliminary action combined with ongoing adjustment resolves the contradiction between initial accuracy and long-term validity.
Solution Approach 2:
The vehicle equipped with the camera system performs its own calibration autonomously during operation using environmental features and sensor fusion. This self-calibration capability eliminates the need for frequent external calibration services, maintaining accuracy over time without additional cost or time loss.
2Adaptability or versatility
If online calibration methods are used based on object extraction, then continuous calibration is possible, but the area of application is restricted and geometric assumptions are required
Solution Approach 1:
The system uses sensor fusion as an intermediary, combining data from multiple sensors (cameras, radar, LIDAR) to perform calibration. This mediator approach allows the system to overcome the limitations of single-sensor methods, expanding applicability while maintaining reliability through cross-validation of multiple data sources.
Solution Approach 2:
The system dynamically adjusts calibration parameters based on operating conditions, environmental features, and sensor performance. By changing parameters adaptively rather than relying on fixed geometric assumptions, the system achieves both broad applicability and high reliability across diverse driving scenarios.
3Measurement precision
If reference sensor technology is used during measurement runs, then high precision extrinsic parameters can be established, but the calibration costs increase
Solution Approach 1:
High-precision reference sensor calibration is performed once during production as a preliminary action to establish accurate initial parameters. This single high-cost calibration event is supplemented by lower-cost online calibration methods, resolving the contradiction between initial precision and ongoing cost.
Solution Approach 2:
The system creates a digital model or copy of the camera's extrinsic parameters during initial reference sensor calibration. This parameter model is then used and refined through online calibration, eliminating the need for repeated expensive reference sensor measurements while maintaining accuracy.
Data Source
AI summary
A method for automatically establishing extrinsic parameters of a camera of a vehicle includes repeatedly establishing the extrinsic parameters of the camera during a measurement run, wherein sensor data are generated by a sensor each time the extrinsic parameters of the camera are established. The established parameters and the associated sensor data respectively are stored in a database. A convolutional neural network is trained based on the extrinsic parameters stored in the database and based on the sensor data. The extrinsic parameters of the camera are subsequently determined online by utilizing the trained convolutional neural network.