Agricultural Vehicle Camera Self-Calibration Using Visual Features
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Solution Overview
Problem
Existing methods for camera calibration on agricultural vehicles are cumbersome, costly, and require precise mounting and well-lit environments, making them inconvenient for operators and prone to inaccuracies.
Innovation Solution
A method for self-calibration of camera pose using visually identifiable features of the vehicle, such as hood creases, ornaments, vents, or purposefully placed fiducials, combined with depth estimation and neural networks, to determine rotational and translational offsets without the need for specific mounting locations or obstructive fiducials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods using laser measuring devices are used, then measurement precision is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent uses visual copying of vehicle features through camera imaging to establish calibration relationships. Instead of direct laser measurement, the system captures images of vehicle features and creates digital representations that are processed to determine camera pose, replacing complex physical measurement devices with optical copying and computational methods
Solution Approach 2:
The patent replaces mechanical laser measuring devices with a vision-based system using cameras and neural networks. The mechanical measurement process is substituted with optical capture, digital image processing, and computational pose estimation, eliminating the need for specialized calibration equipment while maintaining accuracy
2Measurement precision
If traditional calibration methods requiring specific mounting locations are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent creates a universal calibration method that works with cameras mounted at various locations on the vehicle. The neural network model is trained to recognize vehicle features and compute camera pose regardless of mounting position, making the calibration process applicable to multiple camera configurations and locations without requiring specific mounting constraints
Solution Approach 2:
The patent performs preliminary training of neural network models with synthetic images generated from 3D vehicle models before actual calibration. This pre-training phase prepares the system to handle various camera positions and orientations, enabling the calibration to work effectively without requiring precise mounting during actual installation
3Measurement precision
If traditional calibration procedures with obstructive fiducials are used, then measurement precision is improved, but ease of operation and adaptability deteriorate
Solution Approach 1:
The patent uses color-based feature identification where the neural network detects and recognizes colored or distinct vehicle features in images. Instead of requiring obstructive fiducials, the system leverages naturally occurring color variations and visual features on the vehicle that the trained network can identify and use for pose estimation
Solution Approach 2:
The patent enables the calibration system to automatically identify vehicle features and compute camera pose without requiring manual placement of fiducials or user intervention in the calibration procedure. The neural network autonomously detects features and performs pose estimation, making the system self-calibrating and eliminating cumbersome manual calibration steps
Data Source
AI summary
A method and system for self-calibration of camera position on an agricultural vehicle is provided. The method includes the step of identifying at least one visually identifiable structural feature associated with the agricultural vehicle, each of the at least one visually identifiable structural feature having a known pose relative to a coordinate system. The method further includes the step of acquiring imagery with a camera mounted to the agricultural vehicle. The method further includes the step of processing the imagery at a computing device in operative communication with the camera, the imagery comprising the at least one visually identifiable feature, wherein the processing the imagery obtains depth information using the known pose of each of the least one visually identifiable feature and determines a pose of the camera, wherein the pose of the camera comprises a set of rotational offsets and a set of translational offsets relative to the agricultural vehicle.