FINAL POSTER - Ning Wang [CV]

Toward Quadrotor UAV Ubiquity: Achieving Location
Estimation with Inexpensive Hardware
Alex Hagiopol, Josephine Simon, and Ning Wang
Georgia Institute of Technology
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Introduction & Related Work
• Quadrotor unmanned autonomous vehicles can improve the
human condition by autonomously providing delivery, search and
rescue, and information gathering services. Making
inexpensive quadrotor technology increases the chance for
quadrotors to provide improved services to humankind."
• Recent scientific endeavors (Amazon’s Prime Air project,
Google’s Project Wing) are closed-sourced."
• Waypoint navigation has been achieved in both academia and
industry, but with expensive > $1000 closed-source software
and hardware or purely in simulation [1] [2] [5]."
• State estimation informs a quadrotor of its location based on
noisy GPS data. The goal of this work is to make low-cost,
open-source quadrotor location estimation technology
using the inexpensive ($10 to $20), open source, and lowpower Arduino development board. "
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Parts
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Combination
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DJI A2, DJI
!
GPS, Waypoint
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Hardware
Cost
Hovering
Precision
$1500.00 1.5m
Comments
State of the art flight control system. Used in
professional aerial cinematography.
DJI
! Naza, DJI
GPS, Waypoint
Hardware
$500.00 2.5m
Ardupilot, uBlox GPS
$240.00 Unspecified by Programmable, consumer-grade, open-source,
manufacturer flight control system. Used by advanced
hobbyist pilots.
Arduino, DJI
Naza, DJI GPS
$185.00 TBD
Consumer-grade flight control system. Used by
advanced hobbyist pilots.
Proposed combination developed in this
project. Simplest hardware implementation
because Arduino is the only non-DJI
component.
• Using Arduino boards for
navigation can significantly
lower the cost of the control
electronics aboard a quadrotor.!
Noise in Raw Stationary GPS Data
• Raw GPS data are noisy.
Arduino executes a Kalman
filter on the data received from
the attached GPS receiver.
The receiver and Arduino
communicate via a serial link.
A significant challenge is the
Arduino’s low computing
power: Arduino runs at only
16Mhz and has 8KB of RAM.
• The Kalman filter was
chosen because it is a
standard method for
GPS data filtering, as
shown in literature by
[7], [8], and [9].!
• This method recursively
estimates the internal
state of a system by
minimizing the error
covariance of the
estimate. It was
implemented in two
phases.!
• Phase I: Offline Simulation and Tuning We use MATLAB to produce an offline simulation and tune the
process covariance parameters of the Kalman filter."
• Phase II: Online Filtering in Real Time The Arduino implementation performs online Kalman filter position
estimation where GPS data is processed in real-time.
Evaluation Raw GPS Location Data"
& Real Time Arduino Location Estimate
Approach
Zoomed In Raw GPS Location Data"
& Real Time Arduino Location Estimate"
Shows smoothing effect of Arduino Kalman Filter
Discussion & References
The Arduino implementation of the Kalman Filter…!
• …can be executed in real-time on an inexpensive Arduino!
• …can smooth position estimation of discretized data.!
• …can update state estimate even when GPS data is
unavailable."
• …has a final location error close to the hovering precision
of very expensive systems. This means that Arduino can be
a viable replacement for expensive hardware.!
Euclidean Distance From Starting Point "
vs Measurement Count"
Shows interpolation effect of Arduino
Kalman Filter
Performance Comparison with "
State-Of-The-Art Equipment"
Shows potential for future low-cost replacement of!
quadrotor equipment.
Dataset
Final
Location
Error (m)
1
1.85
2
4.02
3
0.471
4
1.24
5
1.18
Average
1.75
Existing
Combination
Price
Hovering
Precision
Claim (m)
$500.00
2.5
$1500.00
1.5
Remaining Issues and Future Work:!
• There is a tradeoff between filtering the GPS noise and
keeping up with fast changes in direction of the quadrotor.
More tuning is required.!
• The control input of the quadrotor is not included in the
system model to make the filtering more reactive to abrupt
direction changes. Improvement of the model is required.!
• A control scheme must be written for Arduino. Now that a
satisfactory location estimation scheme exists, the Arduino
must be programmed to guide the quadrotor to a goal.
[1] F. Kendoulandal., 2009, IEEE Int. Conf. on Robot. and Automat.!
[2] T. Puls, and al., 2009, IEEE Int. Conf. on Robot. and Automat.!
[3] H. Lim and al., 2012, IEEE Robot. Automat. Mag.!
[4] P. Wallich, 2012, IEEE Spectrum [5] 3D Robotics Inc.. 2014. 3DR Products!
[6] S. Russel, P. Norvig, 2010 [7] M. L. Psiakiand and H.Jung, 2002!
[8] J. Z. Sasiadek and al., 2000, IEEE Int. Symp. on Intelligent Cont.!
[9] H. Qi and J.B. Moore, 2002, IEEE Trans. Aerosp. Electron. Syst.!
[10] G. Welch and G. Bishop, 2006.

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