Monocular 3D Field Mapping for Legacy Autonomous Farm Vehicles
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Solution Overview
Problem
Farmers face a financial conflict in transitioning to upgraded mobile field devices with autonomous control capabilities due to high capital costs, necessitating a cost-effective retrofit solution for legacy devices.
Innovation Solution
A relatively inexpensive autonomous control system is installed on legacy mobile field devices, transforming them into upgraded devices with autonomous control capabilities using a monocular camera and AI-based analysis to generate three-dimensional graphics for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If farmers purchase upgraded mobile field devices with autonomous control capabilities, then automation level and operational efficiency are improved, but capital cost increases significantly
Solution Approach 1:
The patent applies this principle by using inexpensive, commercially available components (monocular camera, off-the-shelf computer hardware) to create autonomous control systems for legacy devices, rather than requiring expensive proprietary upgraded equipment. This allows farmers to achieve automation without significant capital investment.
Solution Approach 2:
The patent uses computer-generated three-dimensional graphical representations (virtual copies) of the field environment based on two-dimensional camera images. This copying approach enables the autonomous control system to navigate and operate in the physical field using virtual model data, achieving automation through software-based simulation rather than expensive hardware upgrades.
2Quantity of substance
If a monocular camera is used to capture field environment images, then device cost is reduced, but three-dimensional spatial understanding becomes more difficult to achieve
Solution Approach 1:
The patent transforms two-dimensional camera images into three-dimensional graphical representations through computer processing. This dimensionality transformation allows the system to achieve three-dimensional spatial understanding of the field environment using only a monocular (single-lens) camera, resolving the difficulty of obtaining depth information from 2D images while maintaining low device cost.
Solution Approach 2:
The patent replaces complex mechanical or multi-sensor systems (such as stereo cameras, LIDAR, or multiple sensors) with a simpler computational approach using a single monocular camera and AI-based image processing. This substitution achieves three-dimensional spatial understanding through software algorithms rather than complex hardware mechanisms.
3Extent of automation
If AI-based analysis is used to generate three-dimensional graphics from two-dimensional images, then autonomous navigation capability is improved, but computational processing requirements increase
Solution Approach 1:
The patent implements autonomous navigation where the system independently processes camera images, generates three-dimensional graphical representations, and controls device operation without human intervention. The AI-based analysis automatically performs the computationally intensive tasks of converting 2D images to 3D models and making navigation decisions, enabling full autonomous operation despite high processing requirements.
Data Source
AI summary
The disclosure includes embodiments for an analysis system. A method according to some embodiments is executed by a graphics processing unit. The method includes generating input data including image data captured with a monocular camera operating in a field environment wherein the image data describes a two-dimensional image of the field environment. The method includes analyzing the input data to generate output data describing a three-dimensional graphic of the field environment depicted in the two-dimensional image. In some embodiments, the output data localizes objects, such as a mobile field device upon which the monocular camera is mounted, within the field environment. In some embodiments, the output data localizes any tangible object located within the field environment with an accuracy that satisfies a threshold for accuracy. The method includes modifying an operation of an autonomous control system of a mobile field device based on the output data.