Integrated Vision and IMU Navigation for Real-Time Drift Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Vision-based navigation systems face challenges in providing real-time navigational data such as speed and heading due to high computational requirements and information loss from rolling shutter sensors, which distort images with motion, affecting geometric relationships and making real-time processing impractical and expensive.
Innovation Solution
Integrate an inertial navigation system with a vision-based navigation system to provide real-time navigation data, where the inertial system provides continuous navigation data and the vision-based system corrects the inertial system's output, using a filter to combine estimates and bound drifts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a vision-based navigation system is used to provide navigation data, then measurement precision is improved, but device complexity and computational requirements increase making real-time processing impractical
Solution Approach 1:
The patent combines an inertial navigation system (INS) with a vision-based navigation system into an integrated navigation system. The INS provides continuous navigation data with low computational requirements, while the vision system provides periodic high-precision corrections. This merging allows the system to achieve both real-time performance and high measurement precision without excessive computational complexity.
Solution Approach 2:
The vision-based navigation system operates periodically rather than continuously, processing image frames at intervals to correct INS drift. This periodic operation reduces computational load while maintaining navigation accuracy, as the vision system only needs to compute corrections at specific time points rather than continuously.
2Ease of manufacture
If a rolling shutter sensor is used to reduce cost and semiconductor area, then manufacturing cost is reduced, but information loss occurs due to distortion when the camera or scene is in motion
Solution Approach 1:
The integrated navigation system uses feedback from the INS to compensate for rolling shutter distortion. The INS provides motion compensation data that allows the vision system to correct for the temporal displacement between rows caused by rolling shutter capture, thereby recovering geometric relationship information that would otherwise be lost.
Solution Approach 2:
The INS acts as an intermediary between the rolling shutter sensor and the navigation processing. It provides motion compensation information that mediates the information loss caused by the rolling shutter effect, allowing the vision system to correctly interpret image data despite the distortion.
3Productivity
If vision-based navigation processes images in real-time, then navigation data is provided continuously, but computational effort becomes prohibitively high
Solution Approach 1:
The vision-based navigation processes images periodically rather than continuously in real-time. This periodic processing significantly reduces computational energy requirements while still providing continuous navigation updates through the INS, which operates with minimal computational resources between vision processing intervals.
Solution Approach 2:
The system merges the low-power INS with the high-computation vision system, allowing the INS to handle continuous navigation tasks with minimal energy while the vision system periodically corrects INS drift. This division of labor optimizes the balance between real-time performance and computational energy consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The integrated system achieves near real-time navigation data with lower computational effort, correcting inertial drift and maintaining accuracy by leveraging the strengths of both systems, thus providing practical and cost-effective navigation solutions.
Implementation Method 1
an inertial navigation unit providing a second source of heading and speed estimates for the moving body based on at least one of gyroscopic measurements and accelerometer measurements
Implementation Method 2
a filter combining the heading and speed estimates from the first and second sources to provide an overall system heading and speed estimate for the moving body. In operation, the filter uses the heading and speed estimates from the imaging unit to bound drifts in the heading and speed estimates provided by the inertial navigation unit
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A navigation system providing speed and heading and other navigational data to a drive system of a moving body, e.g., a vehicle body or a mobile robot, to navigate through a space. The navigation system integrates an inertial navigation system, e.g., a system based on an inertial measurement unit (IMU), with a vision-based navigation system unit or system such that the inertial navigation system can provide real time navigation data and the vision-based navigation can provide periodic and accurate navigation data that is used to correct the inertial navigation system's output. The navigation system was designed with the goal of providing low effort integration of inertial and video data. The methods and devices used in the new navigation system address problems associated with high accuracy dead reckoning systems (such as a typical vision-based navigation system) and enhance performance with low cost IMUs.