Vision System Camera Misalignment Calibration
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Solution Overview
Problem
Existing vision systems for autonomous driving and driver assistance face challenges in accurately calibrating the imaging apparatus due to small movements, load changes, and thermal effects, leading to erroneous camera alignment and drift in visual odometry.
Innovation Solution
A statistical analysis method is employed to determine misalignment in the imaging apparatus by representing the vehicle translation vector using spherical coordinates and analyzing odometric data to identify deviations from predefined values, allowing for reliable calibration of the optical axis, particularly when the vehicle moves straight forward.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated dynamic calibration is performed to maintain accurate camera alignment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs self-calibration by automatically detecting misalignment through statistical analysis of odometric data and correcting it without requiring external calibration equipment or manual intervention. The calibration process is integrated into the normal operation of the vision system, allowing it to self-adjust to maintaining accurate alignment.
Solution Approach 2:
The system changes the calibration parameters (camera orientation angles) based on statistical analysis of odometric data. By monitoring deviations in the optical axis orientation over time and adjusting the calibration parameters accordingly, the system maintains measurement precision without complex external calibration mechanisms.
2Measurement precision
If statistical analysis of odometric data is performed to detect misalignment, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system performs statistical analysis on a subset of odometric data points rather than processing all available data continuously. By selecting representative samples for analysis, the system achieves sufficient misalignment detection accuracy while reducing the computational energy required compared to exhaustive data processing.
3Reliability
If continuous calibration is performed to compensate for thermal effects and load changes, then reliability is improved, but productivity decreases
Solution Approach 1:
The system performs calibration at periodic intervals based on statistical analysis of accumulated odometric data rather than continuously. This periodic calibration approach maintains reliability by detecting and correcting misalignment that develops over time due to thermal effects and load changes, while minimizing the impact on productivity by not requiring continuous calibration operations.
Data Source
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AI summary
A vision system (10) and method for autonomous driving and/or driver assistance in a motor vehicle (2) are presented. The vision system (10) comprises an imaging apparatus (11) adapted to capture images (5) from a surrounding (6) of the motor vehicle (2), wherein the imaging apparatus (11) has at least one optical axis (201), and a data processing device (14) performing visual odometry (100) on a plurality of images (5) captured by the imaging apparatus (11) yielding odometric data (101) comprising, or being derived from, the ego vehicle translation vector T (101) for each image (5). The data processing device (14) performs a statistical analysis (104) on the odometric data (101) yielding at least one statistical value, and the data processing device (14) calculates a pitch and/or yaw misalignment (202) of the at least one optical axis (201) by calculating a deviation of said statistical value from a predefined value corresponding to exact and constant forward movement of the ego vehicle (2).