Surround-View Camera Dynamic Calibration via Vehicle Dynamics
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Solution Overview
Problem
Current surround-view camera systems on vehicles cannot recalibrate camera positions and orientations in real-time due to changes in vehicle dynamics, such as loading or impacts, leading to misalignment in top-down views, requiring manual recalibration at service centers.
Innovation Solution
A system and method that integrates sensor data from vehicle dynamics into the video processing module to correct camera calibration by defining a single reference coordinate system, using rotation matrices and translation vectors to adjust camera coordinates, allowing for online recalibration based on changes in pitch, roll, and height.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera calibration is performed during vehicle manufacture using checker-board patterns, then initial camera alignment is achieved, but the system cannot adapt to changes in vehicle dynamics such as loading or impacts
Solution Approach 1:
The system transitions from static calibration performed once during manufacture to dynamic calibration that continuously adapts to changing vehicle conditions. The calibration process is made adaptive by incorporating sensor data from accelerometers and gyroscopes that detect real-time vehicle dynamics changes, allowing the system to automatically recalibrate camera positions and orientations without requiring manual intervention or service center visits.
Solution Approach 2:
The system implements a feedback mechanism where sensor data from vehicle dynamics sensors (accelerometers, gyroscopes) is continuously fed into the calibration algorithm. This feedback loop allows the system to detect changes in vehicle conditions and automatically adjust camera calibration parameters accordingly, maintaining accurate alignment despite loading, impacts, or other dynamic changes throughout the vehicle's operational life.
2Measurement precision
If manual recalibration at service centers is required after vehicle dynamics changes, then calibration accuracy can be restored, but significant time and resource loss occurs
Solution Approach 1:
The system enables self-service calibration by automatically detecting vehicle dynamics changes through onboard sensors and performing recalibration without requiring external service center intervention. The calibration algorithm uses sensor data to autonomously adjust camera parameters, allowing the vehicle to maintain accurate surround-view functionality throughout its operational life without scheduled maintenance or service center visits.
Solution Approach 2:
The system maintains continuous calibration accuracy by performing incremental recalibration whenever vehicle dynamics changes are detected, rather than requiring periodic stops at service centers. This continuous adaptation ensures the surround-view system remains accurate throughout the vehicle's entire operational life, eliminating downtime and maintaining safety functionality without interruption.
3Measurement precision
If camera positions and orientations are adjusted to account for vehicle dynamics, then alignment accuracy is maintained, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using existing vehicle sensors (accelerometers and gyroscopes originally designed for other vehicle dynamics functions) for the additional purpose of camera calibration. This approach avoids adding dedicated calibration hardware while still achieving accurate dynamic recalibration, as the same sensors serve both vehicle stability control and camera alignment functions.
Solution Approach 2:
The system replaces complex mechanical recalibration mechanisms (such as physical adjustment of camera mounts or use of calibration equipment) with computational methods. By using sensor data and algorithms to calculate and apply calibration corrections, the system achieves accurate recalibration through software rather than mechanical intervention, significantly reducing system complexity while maintaining precision.
Data Source
AI summary
A system and method for correcting the calibration of a plurality of cameras on a mobile platform such as in a surround-view camera system on a vehicle based on changes in vehicle dynamics. The method includes reading measurement values from one or more sensors on the vehicle that identify a change in vehicle dynamics and defining the plurality of cameras and a vehicle body as a single reference coordinate system. The method also includes identifying the measured values as a rotation matrix and a translation vector in the coordinate system, and integrating the rotation matrix and the translation vector into a relationship between a vehicle coordinate system and a camera coordinate system to provide the calibration correction of the cameras.