3D Vehicle Imaging for Row-End Detection and GPS-Obstructed Steering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automatic steering systems for vehicles face challenges in precise navigation due to degraded GPS signals from obstructions like trees and buildings, and imaging systems can drift in accuracy when identifying row boundaries and vehicle position, especially with discontinuities in plant growth.
Innovation Solution
A 3-D camera system integrated with visual odometry (VO) and global navigation satellite system (GNSS) data, which uses simultaneous localization and mapping (SLAM) to create a map of the field, allowing the vehicle to navigate accurately even without high-precision GPS, by identifying row centers and obstacles, and performing turns based on fused sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wheel odometry and inertial navigation systems are used to compensate for degraded GPS signals, then vehicle position can be maintained, but the system complexity increases and drift accumulates over time
Solution Approach 1:
The patent combines multiple sensing modalities (GNSS receiver, 3-D camera, inertial measurement unit) into a unified sensor fusion system that processes data from all sources simultaneously. This integration allows the system to maintain reliable position accuracy by cross-validating measurements across different sensors, reducing dependence on any single complex subsystem while compensating for signal degradation through collaborative sensing.
Solution Approach 2:
The patent introduces visual odometry based on 3-D camera data as an intermediary measurement source between direct GNSS positioning and wheel odometry. This intermediary provides independent geometric constraints on vehicle position and orientation, breaking the accumulation of drift errors that would otherwise propagate through the navigation chain, while avoiding the need for complex mechanical encoders or high-precision inertial systems.
2Reliability
If imaging systems are used to identify row boundaries and vehicle position, then navigation can be achieved without GPS, but accuracy drifts due to plant discontinuities and gaps in rows
Solution Approach 1:
The patent implements a feedback mechanism where the 3-D camera continuously monitors row boundary positions and vehicle location, comparing these measurements against the planned path. When discontinuities or gaps are detected that would cause drift, the system adjusts the visual odometry calculations in real-time to compensate for the missing or ambiguous features, maintaining measurement precision despite challenging field conditions.
Solution Approach 2:
The patent transitions from 2-D image analysis to 3-D spatial reasoning by using depth information from the 3-D camera to construct a three-dimensional model of the field environment. This dimensional enhancement allows the system to distinguish between actual row boundaries and visual discontinuities caused by plant spacing or gaps, significantly improving position accuracy by adding geometric context that eliminates ambiguity in row identification.
3Adaptability or versatility
If 3-D camera systems are used for row detection and navigation, then GPS signal obstruction is overcome, but the system requires complex processing of visual odometry and SLAM data
Solution Approach 1:
The patent performs preliminary processing of 3-D camera data by pre-identifying and cataloging row boundaries, plant structures, and field features before navigation begins. This preprocessing creates a reference map of the field geometry that can be quickly matched against real-time camera observations, significantly reducing the computational burden during active navigation while maintaining the ability to operate in GPS-obstructed environments.
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.