position and attitude determination using digital image processing
DESCRIPTION
Position and Attitude Determination using Digital Image Processing. A UROP sponsored research project. Sean VandenAvond. Mentors: Brian Taylor, Dr. Demoz Gebre-Egziabher. Overview. Motivation Introduction Feature Matching Position Determination Google Earth Navigation Simulation - PowerPoint PPT PresentationTRANSCRIPT
Position and Attitude Determination using Digital
Image Processing
Sean VandenAvond
Mentors: Brian Taylor, Dr. Demoz Gebre-Egziabher
A UROP sponsored research project
Overview
• Motivation• Introduction• Feature Matching• Position Determination• Google Earth Navigation Simulation• Future Work
Inertial Measurement Units• Measure linear
accelerations and rotational velocities
• May contain bias error• Error grows over time• $1,000 – $100,000
GPS Update
IMU Readings
IntegrateAttitude andPosition
GPS Update
Error or Noise
• IMU frequency: 50-200 hz• GPS frequency: 1 hz
Problems with GPS• GPS failure can occur in small UAVs
- Interference- Jamming- Spoofing
• University of Texas successfully spoofed an UAV GPS (2012) and an 80 million dollar yacht (2013).
• RQ-170 Sentinel speculated to be spoofed and captured by Iranians (2011)
• Severely limits autonomy and control without backup system.
Motivation• Small UAVs are limited by:
– Price– Weight
• A backup system is required to increase the reliability. This system should be:– Inexpensive– Lightweight– Self-enclosed– Robust and practical
GPS
Images
Position Update
Processing
Proof of ConceptMATLAB
– OpenSURF– Algorithms
Google Earth– KML filesFeature
Matching
Navigation
Algorithms
Simulation
Feature Matching• Feature: unique grouping of pixels in an image.• Given two images the program will output best
estimates on matching features.• Limitations:
- shadows/lighting- lack of unique image data- minimal relative image rotation
These limitations can lead to mismatched feature points between images error in position estimations.
Feature Matching
OpenSURF
Limitations
Shadows and lighting can degrade image matching.
Limitations
No good feature points
Limitations
Pitch = 40º Heading = 40º
Position and Attitude Determination
• PnP problem• Uses known landmark
positions – At least 3 landmarks
• Various PnP algorithms available. – Direct Linear Transform– EPnP– Constrained Least
Square positions.
Depth Ambiguity• Information lost when
converting from 3D scene to 2D image.
• Need to know landmark coordinates in all three dimensions.
• Non unique solution if you don’t have 3rd dimension
• Algorithms breaks down when viewing 2D scene
Limitations
Limitations
Simpler Solution• Let Heading = Pitch = Roll = 0º
Position Determination
Use image matching and FOV calculations to determine new position.
Resulting Position
Attitude Error
• Pitch of 5 degrees: estimated position error of
Simulation• Full guidance and navigation simulation
in Google Earth using MATLAB• Assumptions:
- Heading = Pitch = Roll = 0- Neglecting aircraft dynamics- Fixed velocity- Fixed altitude
Initial image at known lat/lon
Destination
New heading
“AircraftSimulation”
New image
Feature matching
Estimate new lat/lon
Old image
Results
Matched Images
Mismatch Error
Percent Overlap
Image 1 Image 2Overlap
More Overlap:- Better matches- Slower
Tests done with 3000 foot length step overlap of about 25 percent
Summary• Over 100 miles in simulation.• Show promise for using digital image processing for a
backup navigation system.• Benefits
- Lightweight- Inexpensive- Self-enclosed
• Limitations- Needs unique feature points- Computationally expensive
Future Work
• Couple simulation with UAV lab MATLAB script to include errors in sensors.
• Post-processing from UAV lab flight tests• Adapt to allow onboard flight testing
Results