Vehicle Cabin Camera Calibration Using Symmetry and 3D Orientation Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for calibrating vehicle cabin cameras, such as those integrated into rear-view mirror assemblies, face challenges in accurately determining camera orientation in 3D space, particularly when relying on multiple sensors that consume power and require additional cables, and often suffer from high numerical complexity and local minima issues during calibration.
Innovation Solution
A method that combines visual extrinsic calibration using a CAD model and inertial measurements from accelerometers to reduce numerical complexity and accurately determine camera orientation in all three degrees of freedom, leveraging symmetry in the vehicle cabin and gradient-based optimization techniques to minimize local minima, and incorporates a single accelerometer to supplement orientation detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are incorporated inside the mirror or joint to determine camera orientation, then measurement precision is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent extracts the orientation measurement function from multiple complex sensors and implements it through a simplified visual calibration method using a single accelerometer combined with CAD model processing. The calibration pattern and image processing externalize the measurement function, eliminating the need for multiple internal sensors.
Solution Approach 2:
The patent replaces the mechanical sensor-based measurement system with a visual-computational system. Instead of using multiple physical sensors to detect orientation, the system uses a single accelerometer combined with visual calibration against a CAD model, substituting mechanical measurement with optical and computational methods.
2Measurement precision
If multiple sensors and cables are added to the mirror assembly, then camera orientation detection capability is improved, but ease of operation deteriorates due to additional supply and signal cables
Solution Approach 1:
The patent extracts the orientation detection capability from the mirror assembly hardware and relocates it to a software-based calibration process. The calibration pattern and image processing externalize the detection function, eliminating the need for additional cables and power supply connections within the mirror assembly.
Solution Approach 2:
The system performs self-calibration by capturing images of a known calibration pattern and automatically computing camera orientation parameters through image processing and CAD model comparison. This self-service approach eliminates the need for manual calibration procedures and additional wiring.
3Device complexity
If a CAD-only approach is used for camera orientation measurement, then device complexity is reduced, but numerical complexity increases by an order of magnitude
Solution Approach 1:
The patent introduces a single accelerometer as an intermediary element that bridges the gap between simple device architecture and accurate orientation measurement. The accelerometer provides gravitational reference data that simplifies the numerical optimization process when combined with CAD model processing, reducing computational complexity while maintaining device simplicity.
Solution Approach 2:
The patent changes the measurement parameters by incorporating accelerometer data into the calibration process. This additional parameter (accelerometer reading) provides a gravitational reference that constrains the optimization problem, reducing the search space and numerical complexity compared to pure visual CAD-only approaches.
4Device complexity
If inertial measurement alone is used, then device complexity is reduced, but measurement precision deteriorates as it only provides two degrees of freedom
Solution Approach 1:
The patent merges inertial measurement (single accelerometer) with visual measurement (camera images of calibration pattern) to achieve complete three-degree-of-freedom orientation detection. The combination of these two measurement modalities compensates for the limitations of each individual method, providing both simplicity and completeness.
Solution Approach 2:
The single accelerometer serves multiple functions: it provides gravitational reference for pitch and roll detection, aids in initial orientation estimation, and constrains the calibration optimization problem. This multi-functional use of a single sensor maximizes its utility while maintaining device simplicity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method significantly reduces numerical complexity by an order of magnitude when a CAD model is available and two orders of magnitude when not, allowing for reliable camera orientation detection in all three degrees of freedom, while also being more efficient than traditional CAD-only approaches.
Implementation Method 1
a single accelerometer housed within the camera or mirror
Data Source
AI summary
A method for calibrating a vehicle cabin camera having: a pitch, yaw and roll angle; and a field of view capturing vehicle cabin features which are symmetric about a vehicle longitudinal axis comprises: selecting points from within an image of the vehicle cabin and projecting the points onto a 3D unit circle in accordance with a camera projection model. For each of one or more rotations of a set of candidate yaw and roll rotations, the method comprises: rotating the projected points with the rotation; flipping the rotated points about a pitch axis; counter-rotating the projected points with an inverse of the rotation; and mapping the counter-rotated points back into an image plane to provide a set of transformed points. A candidate rotation which provides a best match between the set of transformed points and the locations of the selected points in the image plane is selected.