Vehicle Cabin Camera Calibration Using Symmetry and Inertial Sensing
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Solution Overview
Problem
Current methods for calibrating vehicle cabin cameras are inefficient due to high numerical complexity and reliance on additional sensors or cables, and existing techniques struggle to accurately determine camera orientation in three degrees of freedom, especially without a CAD model or when vehicle symmetry is not assumed.
Innovation Solution
A method that reduces numerical complexity by using a combination of inertial measurements from accelerometers and visual symmetry-based approaches, allowing for camera orientation calibration in all three degrees of freedom, even without a CAD model, by projecting image pixels onto a unit sphere and employing objective functions to identify optimal rotations, and utilizing inertial measurements to determine the X-axis rotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors (accelerometers, gyroscopes, magnetometers) are incorporated inside the mirror or joint to determine camera orientation, then measurement precision is improved, but device complexity and power consumption increase due to additional supply and signal cables
Solution Approach 1:
The patent extracts the orientation measurement function from complex multi-sensor systems and implements it through a simplified visual calibration method using camera images and symmetry plane detection. The measurement capability is taken out from the physical sensor assembly and achieved through image processing algorithms instead.
Solution Approach 2:
The patent replaces the mechanical sensor-based measurement system with an optical/image-based system. Instead of using accelerometers, gyroscopes, and magnetometers to determine camera orientation, the system uses camera images of the vehicle interior, detects symmetry planes, and calculates orientation through visual feature matching and optimization algorithms.
2Measurement precision
If a CAD-only approach is used to calculate camera orientation, then measurement precision can be achieved, but numerical complexity increases by an order of magnitude compared to hybrid approaches
Solution Approach 1:
The patent segments the orientation calculation problem into two independent parts: (1) determining the symmetry plane using visual features from camera images, and (2) calculating the remaining rotation about the symmetry plane normal using inertial measurements. This segmentation reduces the overall numerical complexity by breaking down the complex optimization problem into simpler sub-problems.
Solution Approach 2:
The patent changes the parameter representation by identifying and utilizing the symmetry plane of the vehicle interior as an intermediate parameter. By first determining the symmetry plane orientation from visual features and then using it to simplify the remaining orientation calculation, the system reduces numerical complexity while maintaining accuracy.
3Device complexity
If inertial measurement alone is used to determine camera orientation, then device complexity is reduced, but measurement precision deteriorates because accelerometers provide only two degrees of freedom instead of three
Solution Approach 1:
The patent merges two different measurement approaches: visual symmetry plane detection (providing two degrees of freedom) and inertial accelerometer measurements (providing one degree of freedom about the symmetry plane normal). By combining these complementary measurements, the system achieves full three-degree-of-freedom orientation determination while keeping the device complexity low.
4Ease of operation
If visual symmetry-based calibration is used without CAD models, then ease of operation is improved, but measurement precision may be insufficient without additional sensor data
Solution Approach 1:
The patent enables the calibration system to be self-sufficient by using visual features from the camera images themselves to determine the symmetry plane, without requiring external CAD models or additional specialized sensors. The system services its own calibration needs using the camera's existing imaging capability and image processing algorithms.
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
This method significantly reduces numerical complexity, allowing for reliable camera orientation calibration in all three degrees of freedom, with a two-order magnitude reduction in complexity compared to CAD-only approaches and a one-order magnitude reduction when combining CAD data with symmetry assumptions, while also being efficient in terms of hardware requirements.
Implementation Method 1
determine an X axis rotation angle of the camera about the X axis relative to the vehicle using an accelerometer housed within the camera or mirror
Data Source
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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.