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