3D Vehicle Imaging for Row-End Detection and GPS-Limited Navigation
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Solution Overview
Problem
Existing vehicle control systems face challenges in maintaining precise navigation due to GPS signal degradation under obstructions and inaccuracies in identifying row boundaries and vehicle location, leading to issues in agricultural applications.
Innovation Solution
A vehicle control system utilizing 3-D cameras, visual odometry, and GNSS data to enhance navigation by creating a simultaneous localization and mapping (SLAM) map, integrating sensor fusion for accurate row detection and obstacle avoidance, even in areas with limited GPS signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wheel odometry and inertial navigation systems are used to maintain GPS position when signals degrade, then vehicle location can be maintained, but the system complexity increases and measurement precision deteriorates due to cumulative drift errors
Solution Approach 1:
The patent replaces mechanical/wheel-based odometry systems with an optical imaging system that uses computer vision to detect row features and calculate vehicle position. This substitution eliminates the need for complex mechanical sensors and inertial navigation systems while maintaining location accuracy through visual feature tracking against a pre-stored map.
Solution Approach 2:
The patent creates a digital copy of the field environment by storing a map of row locations and characteristics before the vehicle enters the field. This pre-captured visual map serves as a reference that the imaging system continuously compares against real-time images to determine precise vehicle location without drift accumulation.
2Measurement precision
If imaging systems are used to identify row boundaries and vehicle location, then navigation information can be obtained, but measurement precision deteriorates when plants extend over adjacent rows or form gaps within rows
Solution Approach 1:
The patent segments the row detection problem into multiple hierarchical levels: first detecting the overall row structure, then identifying specific feature points along the row, and finally using these segmented features to calculate vehicle position. This multi-level segmentation allows the system to handle discontinuities by relying on the broader row structure rather than requiring every individual plant to be perfectly aligned.
Solution Approach 2:
The patent implements feedback by continuously comparing real-time imaging data with the pre-stored map of expected row locations. When discrepancies are detected due to plant variations, the system uses the map as a reference to correct and refine the vehicle location estimate, ensuring consistent accuracy even when visual features are imperfect.
3Adaptability or versatility
If 3-D laser scanners or stereo cameras are used to detect rows and obstructions, then field navigation capability is improved, but the system complexity and cost increase significantly
Solution Approach 1:
The patent uses a standard monocular camera instead of expensive 3-D laser scanners or stereo camera systems. While a single image frame contains limited depth information, the system achieves robust 3-D understanding by tracking the temporal sequence of images and leveraging the pre-stored map, effectively replacing expensive hardware with a more economical solution.
Solution Approach 2:
The patent makes the imaging system universal by using a standard camera that can perform multiple functions: detecting row boundaries, identifying obstructions, tracking vehicle position, and navigating turns. This single device replaces what would traditionally require multiple specialized sensors, reducing overall system complexity while maintaining versatile field detection capability.
Data Source
AI summary
A control system uses visual odometry (VO) data to identify a position of the vehicle while moving along a path next to the row and to detect the vehicle reaching an end of the row. The control system can also use the VO image to turn the vehicle around from a first position at the end of the row to a second position at a start of another row. The control system may detect an end of row based on 3-D image data, VO data, and GNSS data. The control system also may adjust the VO data so the end of row detected from the VO data corresponds with the end of row location identified with the GNSS data.