Vehicle Camera Calibration Targets for Real-Time Stereo Depth
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Solution Overview
Problem
Existing camera systems used for stereoscopic depth determination suffer from alignment and positioning issues due to vibrations and temperature changes, affecting the accuracy of depth determinations, particularly in moving vehicles.
Innovation Solution
Implementing a vehicle-mounted calibration target, such as an AprilTag or QR code, within the field of view of the cameras, which allows for real-time frame-to-frame calibration by using captured images to update camera positions and parameters, compensating for environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cameras are mounted on a moving vehicle for stereoscopic depth determination, then the system can perform depth measurements in dynamic environments, but vibrations and temperature changes cause camera alignment and positioning drift that degrades measurement accuracy
Solution Approach 1:
The system performs preliminary calibration by capturing images of a known calibration target (such as a chessboard or AprilTag) before performing depth determination. This preliminary action establishes reference camera parameters and positions, allowing the system to compensate for subsequent vibrations and temperature changes during vehicle operation.
Solution Approach 2:
The system continuously monitors camera alignment by repeatedly capturing images of the calibration target during operation and comparing the observed features against known reference data. This feedback loop enables real-time detection of position drift and triggers recalibration when degradation exceeds thresholds, maintaining measurement precision despite environmental disturbances.
2Reliability
If camera calibration is performed frequently (frame-to-frame) to maintain accuracy under environmental changes, then depth determination reliability improves, but system complexity and processing time increase
Solution Approach 1:
Instead of performing full calibration sequences frame-to-frame, the system uses partial calibration actions by detecting key features of the calibration target (such as corner points or marker positions) and updating only the necessary camera parameters. This selective approach maintains reliability by correcting drift when detected, while avoiding the excessive complexity of complete recalibration at every frame.
Solution Approach 2:
The system performs calibration updates periodically based on detected drift thresholds rather than continuously at every frame. When the calibration target is detected and position drift exceeds a predetermined threshold, the system triggers a calibration update. This periodic action maintains reliability by correcting significant drift events while reducing system complexity by avoiding unnecessary continuous recalibration.
3Measurement precision
If a calibration target is mounted on the vehicle within camera view to enable real-time calibration, then camera position updates can be performed continuously, but the target may be visible to drivers or interfere with vehicle operation
Solution Approach 1:
The calibration target is positioned in a specific local area of the vehicle (such as the hood or windshield area) where it is within camera view for calibration purposes but minimally visible or invisible to drivers. The target uses patterns or colors that are easily detectable by camera sensors but blend with or are obscured from human visual perception during normal driving conditions.
Solution Approach 2:
The system uses digital representations or processed versions of the calibration target images for calibration purposes rather than relying on the physical target's visual appearance. Image processing algorithms extract calibration features from captured frames, allowing the physical target to be optimized for machine detection while its visual impact on drivers is minimized or eliminated through strategic placement and design.
Data Source
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.