Stereo Camera Calibration Targets for In-Field Depth Accuracy
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Solution Overview
Problem
Existing stereoscopic camera systems on vehicles face challenges in maintaining accurate depth determination due to vibrations, temperature changes, and other movements that affect camera alignment and positioning, necessitating frequent calibration and updates while in the field.
Innovation Solution
A vehicle-mounted calibration target, such as an AprilTag or QR code, is used to facilitate real-time camera calibration by determining spatial relationships between cameras, allowing for frame-to-frame updates and compensation for environmental changes, enhancing stereoscopic depth determination accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera calibration is performed only at initial deployment, then device complexity is reduced, but measurement precision deteriorates due to vibrations and temperature changes affecting camera alignment
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple possible camera positions and calibration parameters before the vehicle operates. During runtime, the system simply selects from pre-computed calibration data based on detected camera position, avoiding real-time complex calculations and enabling rapid adaptation to environmental changes without increasing operational complexity
Solution Approach 2:
The system dynamically adapts camera calibration by detecting actual camera positions during vehicle operation and selecting appropriate calibration parameters from pre-defined sets. This allows the system to transition from static initial calibration to dynamic adaptive calibration, maintaining measurement precision despite vibrations and temperature changes
2Measurement precision
If frequent calibration updates are performed while in the field, then measurement precision is improved, but loss of time increases due to repeated calibration processes
Solution Approach 1:
The patent resolves the time-loss contradiction by performing calibration computations in advance during system setup or manufacturing. Multiple calibration scenarios are pre-calculated and stored, so during field operation the system only needs to detect camera position and retrieve corresponding calibration data, reducing calibration time from minutes to milliseconds while maintaining high precision
Solution Approach 2:
The system implements dynamic calibration selection where the appropriate calibration parameters are automatically chosen based on real-time camera position detection. This eliminates the need for time-consuming manual recalibration while adapting to environmental changes, achieving both speed and accuracy
3Reliability
If camera positions are fixed and not updated, then device complexity is minimized, but reliability deteriorates under environmental conditions like vibrations and temperature changes
Solution Approach 1:
The patent implements dynamic calibration parameter selection based on detected camera positions. The system continuously monitors camera alignment and automatically selects appropriate calibration parameters from pre-defined sets, enabling the system to adapt to environmental changes without requiring complex real-time recalibration algorithms or additional hardware
Solution Approach 2:
By pre-computing and storing multiple calibration parameter sets corresponding to different camera positions, the system prepares in advance for environmental variations. During operation, it simply retrieves the appropriate pre-computed calibration data based on detected position, achieving high reliability without adding operational complexity
Data Source
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AI summary
Calibration target(s) are mounted on a vehicle including a plurality of cameras. The calibration targets are in the field of view of the cameras. The calibration targets are sometimes LED or IR illumined targets. The targets in some embodiments are shielded to block light from oncoming cars and/or to block driver view of the target to reduce the risk of driver distraction. The cameras capture images at the same time. Calibration operations are then performed using the target or targets included in the captured images as a known visual reference of a known shape, size and/or having a known image pattern. Calibration parameters including parameters providing information about the location and/or spatial positions of the cameras with respect to each other and/or the target are generated from the captured images. Distance to objects in the environment which are included in the captured images are then determined.