Multi-Sensor Calibration Using FOE for Faster SVM Alignment
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Solution Overview
Problem
Existing vehicle surround view monitor (SVM) systems face challenges in efficiently performing camera calibration during the production process, requiring precise alignment with reference objects, which increases calibration time and operational complexity.
Innovation Solution
A method for sensor calibration that estimates rotation and translation parameters using sensing data from multiple sensors, including cameras, lidar, and ultrasonic sensors, allowing calibration even when the vehicle is not perfectly aligned with reference objects, by determining extrinsic parameters through a combination of intrinsic sensor properties and focus of expansion (FOE) analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional camera calibration methods are used requiring precise alignment with reference objects, then manufacturing precision is improved, but calibration time and operational complexity increase
Solution Approach 1:
The patent replaces the mechanical alignment system (physical centering devices and manual positioning) with a computational vision system that uses image processing and coordinate transformation algorithms. The processor automatically determines relative positions and orientations by analyzing images of reference objects, eliminating the need for mechanical precision alignment equipment and manual centering operations.
Solution Approach 2:
The calibration system performs self-alignment through automated image processing. The processor independently calculates extrinsic parameters by detecting reference objects in images, computing coordinate transformations, and determining sensor positions and orientations without requiring external mechanical alignment assistance or manual intervention for positioning.
2Manufacturing precision
If traditional camera calibration methods are used requiring precise alignment with reference objects, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical alignment systems with a software-based computational approach. Instead of using physical centering devices, alignment markers, and manual positioning mechanisms, the system uses image processing algorithms and coordinate transformation mathematics to achieve precise calibration, thereby reducing mechanical device complexity.
Solution Approach 2:
The calibration system is designed to be universal and adaptable to different sensor types and configurations. The same image processing and coordinate transformation methodology can be applied to various camera models, sensor arrangements, and reference object types, eliminating the need for specialized equipment for each specific calibration scenario.
3Manufacturing precision
If traditional camera calibration methods are used requiring precise alignment with reference objects, then manufacturing precision is improved, but ease of operation deteriorates
Solution Approach 1:
The calibration process is fully automated through the processor that independently performs image analysis, coordinate transformation, and parameter calculation. The system self-corrects for misalignments and automatically determines the correct extrinsic parameters without requiring operator skill for precise manual alignment or complex procedural steps.
Solution Approach 2:
The patent replaces manual mechanical alignment operations with automated computational methods. The processor uses image processing algorithms to automatically detect reference objects and calculate transformations, eliminating the need for operators to perform delicate manual positioning or interpret complex alignment requirements.
Data Source
AI summary
A processor-implemented method with sensor calibration includes: estimating a portion of a rotation parameter for a target sensor among a plurality of sensors based on a capture of a reference object; estimating another portion of the rotation parameter for the target sensor based on an intrinsic parameter of the target sensor and a focus of expansion (FOE) determined based on sensing data collected with consecutive frames by the target sensor while the electronic device rectilinearly moves based on one axis; and performing calibration by determining a first extrinsic parameter for the target sensor based on the portion and the other portion of the rotation parameter.