Binocular Camera Self-Calibration for Extrinsic Drift Correction
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Solution Overview
Problem
Existing binocular cameras in intelligent vehicles face issues due to extrinsic parameter changes caused by environmental factors like vibrations and temperature fluctuations, leading to calibration errors, and the use of rigid connections increases cost, bulkiness, and reduces flexibility in installation.
Innovation Solution
A self-calibration method for binocular cameras without rigid connections, involving image analysis to detect calibration anomalies and correct extrinsic parameters using a grid search method to adjust rotation matrices, reducing the need for costly and bulky rigid structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a rigid connection structure is used between the two cameras, then the extrinsic parameters stability is improved, but the cost increases and the device becomes bulky
Solution Approach 1:
The patent replaces the mechanical rigid connection structure with a software-based self-calibration system. Instead of using physical rigid structures to maintain extrinsic parameter stability, the system uses image processing and calibration algorithms to detect and correct parameter changes caused by environmental factors, thereby eliminating the need for complex mechanical structures while maintaining reliability
Solution Approach 2:
The patent implements self-calibration functionality where the binocular camera system automatically detects and corrects its own extrinsic parameter deviations. The system captures images, calculates calibration errors, and adjusts parameters without external intervention, enabling the system to maintain accuracy autonomously without requiring rigid structural constraints
2Reliability
If a rigid connection structure is used between the two cameras, then the extrinsic parameters stability is improved, but the installation space increases
Solution Approach 1:
The patent replaces space-consuming mechanical rigid connection structures with a compact software-based calibration system. By using image processing and algorithmic correction instead of physical structures, the system achieves extrinsic parameter stability without requiring additional installation space
3Reliability
If a rigid connection structure is used between the two cameras, then the extrinsic parameters stability is improved, but the flexibility in installation is reduced
Solution Approach 1:
The patent replaces rigid mechanical connection structures with flexible software-based calibration. This substitution allows the camera system to be installed in various positions and configurations without being constrained by rigid structural requirements, while the self-calibration algorithm ensures parameter stability regardless of installation variations
Solution Approach 2:
The patent introduces dynamic self-calibration capability that adapts to different installation conditions. The system can automatically adjust to various mounting positions, angles, and environmental conditions through real-time calibration, providing flexibility while maintaining reliability
4Device complexity
If self-calibration is implemented without rigid connections, then the device complexity is reduced and flexibility is improved, but calibration accuracy may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors calibration status by capturing images, calculating calibration errors, and comparing results against acceptable thresholds. Based on this feedback, the system automatically triggers recalibration when deviations are detected, ensuring measurement precision is maintained without requiring rigid connections
Solution Approach 2:
The patent performs preliminary calibration actions by establishing initial extrinsic parameters and creating a baseline for comparison. The system proactively monitors for drift and performs corrective calibration before significant accuracy degradation occurs, maintaining precision throughout operation
Data Source
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AI summary
The present application discloses a binocular camera self-calibration method, a computer device, a storage medium, a program product, and a mobile platform. The binocular camera includes a first camera and a second camera, wherein there is no rigid connection between the first camera and the second camera. The method includes: acquiring a first image captured by the first camera and a second image captured by the second camera; determining whether there is a calibration anomaly between the first camera and the second camera based on the first image and the second image; correcting extrinsic parameters of the first camera and the second camera when it is determined that there is the calibration anomaly between the first camera and the second camera. Due to vibration, temperature changes, and other factors, the extrinsic parameters of the binocular camera without a rigid connection may change, causing errors in the data captured by the binocular camera. The present application addresses this problem by checking for the calibration anomaly of the binocular camera and correcting the extrinsic parameters when the calibration anomaly is determined.