Autonomous Farming Vehicle Camera Calibration Using 3D Reference Alignment
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Solution Overview
Problem
Conventional farming machines face challenges in calibrating their image acquisition systems due to varying environments, camera characteristics, and placements, which affects the accuracy of image interpretation and navigation during farming operations.
Innovation Solution
A farming machine performs real-time calibration of its image acquisition system by generating a 3D model using image segmentation, inertial measurement unit (IMU) data, and global positioning system (GPS) data, comparing represented locations to reference locations to adjust camera positions, orientations, or digital settings for precise alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional farming machines use standard calibration procedures, then calibration can be performed, but accuracy deteriorates due to varying environments, camera characteristics, and placements
Solution Approach 1:
The calibration method transitions from static pre-defined standards to dynamic real-time calibration. The system continuously captures images during field operations, performs real-time object detection and 3D model generation, and dynamically adjusts calibration parameters based on current environmental conditions and camera configurations, enabling adaptation to varying fields, lighting, and camera placements
Solution Approach 2:
The farming machine performs self-calibration using its own image acquisition system, IMU, and GPS without requiring external calibration equipment or personnel. The system automatically detects objects in the field, generates 3D models, compares them with reference models, and determines calibration adjustments autonomously, making the calibration process self-sufficient and adaptable to different operating conditions
2Measurement precision
If real-time calibration is performed using multiple sensors and processing, then calibration accuracy improves, but system complexity increases
Solution Approach 1:
The system uses a multi-functional integrated approach where the image acquisition system serves both farming operation guidance and calibration purposes. The same cameras, processors, and algorithms used for field analysis are also employed for calibration, eliminating the need for separate dedicated calibration equipment and reducing overall system complexity while maintaining high precision
Solution Approach 2:
The calibration process implements continuous feedback loops where the system compares real-time 3D model data with reference model data, automatically determines calibration adjustments, and applies corrections. This closed-loop feedback mechanism enables high-precision calibration through iterative refinement without requiring overly complex manual intervention systems
Data Source
AI summary
A system and a method are disclosed for calibrating an image acquisition system of a farming machine. The farming machine captures images of objects in an environment as the farming machine moves through an environment. Based on the captured images, a represented three-dimensional (3D) model of the environment representing the objects in a three-dimensional space is generated. From the represented 3D model, a represented location of an object (e.g., building, lamppost, tree) is determined. The represented location of the object is compared to a reference location of the object and the comparison is used to calibrate the image acquisition system by modifying positions, orientations, or optical settings of one or more cameras in the image acquisition system.