Vehicle Range-Finding Parallax Calibration via Lateral Deviation Correction
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Solution Overview
Problem
Existing range-finding systems using stereo camera systems in vehicles face challenges in accurately measuring lateral image deviation due to factors like camera tilt and changes in distortion characteristics, especially when a known test chart is not available during use, making it difficult to correct non-uniform image deviations without using a test chart.
Innovation Solution
A range-finding system with two imaging devices and a parallax calculator that includes an estimation unit to calculate correction values based on pixel position-specific image deviations, allowing for correction of pre-correction parallax or images, thereby addressing lateral image deviations without relying on known objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two cameras are mounted in a vehicle for range-finding, then the system can measure distance and detect obstacles, but the cameras cannot be disposed precisely in parallel without error, leading to inaccurate parallax measurement
Solution Approach 1:
The patent applies preliminary action by performing calibration before actual range-finding operations. The system captures images of a calibration chart at multiple positions, calculates parallax values in advance, and creates correction data that compensates for mounting errors. This pre-processing step ensures that even though the cameras are not perfectly parallel due to manufacturing limitations, the parallax measurements remain accurate through the use of pre-calculated correction factors.
2Measurement precision
If parallax correction techniques using signal processing are used to correct non-linear deviation, then image deviation can be corrected, but a known test chart is required which is difficult to capture during actual use
Solution Approach 1:
The system applies self-service by using the vehicle's existing imaging infrastructure to capture calibration data during normal operations. Instead of requiring external test charts or specialized calibration equipment, the system uses naturally occurring features in the captured images (such as road markings, lane dividers, or other environmental features) as calibration references. The processor automatically identifies these features and performs calibration without human intervention, making the system self-calibrating and eliminating the need for manual test chart capture.
3Manufacturing precision
If affine conversion is used to modify image data for pseudo-parallel arrangement, then the arrangement can be corrected, but non-linear deviation such as optical lens aberration cannot be corrected
Solution Approach 1:
The patent applies parameter changes by transitioning from simple affine conversion parameters to more complex non-linear transformation parameters. The system calculates parallax values at multiple different positions and uses these to determine correction factors that account for non-linear effects such as lens aberration. By changing the mathematical model from linear affine transformation to non-linear transformation based on multiple measurement points, the system can correct both the camera arrangement errors and the optical distortion simultaneously, achieving comprehensive image deviation correction.
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 system effectively corrects non-uniform image deviations in the lateral direction, improving the accuracy of parallax measurement and range-finding capabilities without the need for a test chart, ensuring consistent and precise distance calculations.
Implementation Method 1
A stereoscopic image of the area in front of the vehicle is generated using the stereo camera systems, and an obstacle is detected and a distance to the obstacle is measured based on the generated stereoscopic image
Data Source
AI summary
A range-finding system includes two imaging devices to capture multiple images from two different viewpoints and a parallax calculator to calculate parallax based on the multiple images captured by the two imaging devices. The parallax calculator includes an estimation unit to estimate a correction value based on the amount of image deviation in a lateral direction corresponding to pixel position in the images captured by the two imaging devices and a correction unit to correct a pre-correction parallax or an image based on the correction value estimated by the estimation unit.