whitepaper.tex

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\title{Piksi for UAV Aerial surveying}
\mysubtitle{RTK direct georeferencing with Swift Navigation's Piksi GPS receiver}
\author{Dennis Zollo, Rai Gohalwar}
\date{\today}

\ignorespaces

\begin{document}
\maketitle

\thispagestyle{firstpage}

\section{Abstract}
\label{sec:abstract}
This whitepaper presents using Piksi, a carrier phase differential GPS sensor, to georeference
 images from micro aerial vehicles (MAVs) in surveying use cases.
It presents both the sensor integration, data collection methods, and real-world surveying results
as processed by the PIX4D photogrammetry software.  Lastly, the value proposition of using RTK GPS
for aerial surveying is evaluated.
\tableofcontents
\newpage
\section{Overview}
\label{sec:Overview}
Due to the capability, low-cost, and popularity of micro aerial vehicles aerial surveying in industries such as 
precision agriculture, mining, and forestry is of great interest.

In a typical aerial surveying use-case, an aircraft is outfitted with a high-quality camera and
overflies an area of interest while capturing a series of images.  The images are then processed in
software to produce Digital Elevation Models (DEM's), Orthomosaics, and/or 3D point clouds which
can be used for photogrammetry applications, volumetric measurements, or crop health analysis to
provide business value for users.

Commercial software tools for photogrammetry have the ability to stitch together aerial images
through visual features with techniques such as Bundle adjustment.  Additionally, these software packages often
require rough location and orientation of the lense when the photo was taken.

To facilitate post-processing, most low-cost MAV control systems used for photogrammetry have
the ability to geotag photos as required by the processing software through Autonomous GPS combined
with MEMS sensors.  The typical sensor technology, however, combined with uncertainty in timing of the
camera's shutter, limits the precision and accuracy of geotagging information and therefore requires
post-processing software to rely heavily on image processing techniques. Additionally, large
amounts of sidelap and overlap between images and ground control points are often required to allow
post-processing software to utilize imagery information given the inaccuracy of the georeferencing
information.  Lastly, survey sites lacking in visual detail (such as agricultural land) or where
overlap is minimal (such as corridor mapping), often yield poor results with traditional techniques
and sensors.

It has been demonstrated that Real Time Kinematic (RTK) GPS (also called Carrier Phase Differential
GPS), can improve the location accuracy of georeferencing \cite{sensefly2}.  In the sections that
follow, we will demonstrate methods and results of one such RTK sensor, Swift Navigation's Piksi to
geotag aerial photos for aerial surveying.  It is expected that precise and accurate geotagging
information can reduce the need for ground control points for typical survey missions, reduce the
amount of overlap and sidelap required, and improve the quality of ultimate photogrammetry
deliverables.

\section{Equipment and Setup}
\label{sec:equipment}
\begin{figure}[h]
\includegraphics[width=7in]{images/flow_charts/uav_piksi_flow_chart.png}
\caption{Vehicle Diagram}
\label{figure:vehicle-diagram}
\end{figure}

\label{sec:equipment}
A camera, vehicle, and an image-tagging system using Piksi were developed to conduct experiments
with careful design considerations.  Available and low cost COTS equipment was chosen to highlight
that these results can be replicated without exotic or expensive equipment.  

A Sony NEX-5t mirorrless DLSR Camera with a fixed 20mm lense and a 16 MP CCID sensor was used as an imaging system. See table \ref{cameraspecs} for detailed camera specifications. The application also required the
ability to electrically sense the shutter which was achieved through the use of a Fotasy SANEX Hot
Shoe Adapter Prontor/Compur (PC) socket for external flash synchronization.  The digital flash signal was
routed from the PC socket to the "external event" trigger feature of the Piksi receiver (pin 0 of Piksi's debug 
connector.)

The vehicle was designed and sized to carry the camera payload for a typical surveying mission.
While a fixed wing aircraft may be more applicable to surveying missions for their increased range,
a quadrotor configuration was chosen for low-cost and ease of implementation.  The test vehicle is
based on a 680 Tarot quad frame and uses four TigerMotor antigravity 4006 motors with 15 inch
propellers. The Pixhawk autopilot controls the aircraft and a 10.4Ah 6S battery pack powers. Fully
loaded the copter has a flight time of about 30 minutes. The Sony camera is attached pointing down
via a custom designed 3D printed a housing. Communication to both a base station Piksi and a UAV
Ground control station was accomplished via two 3D Robotics point to point radio modems. See table
\ref{table:vehicle-specs} for a summary of vehicle configuration.

As an autopilot and flight controller, the Pixhawk flight controller running Arducopter version 3.3.2 was used.
A Ublox NEW 7N gps receiver functioned as a backup GPS sensor and as a control against the Piksi sensor. See table \ref{table:gps} for more information about the GPS sensors and antennas integrated on the aircraft.

\begin{figure}
\centering
\renewcommand*\thesubfigure{\arabic{subfigure}}
\begin{subfigure}{.5\textwidth}
  \centering
  \includegraphics[width=1\linewidth]{images/top_view.png}
  \caption{Top-View}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.5\textwidth}
  \centering
  \includegraphics[width=1\linewidth]{images/side_view.png}
  \caption{Bottom-View}
  \label{fig:sub2}
\end{subfigure}
\caption{Surveying UAV}
\label{ortho}
\end{figure}

\begin{table}[]
\centering
\begin{tabular}{l^l}
\hline
\rowstyle{\bfseries}
Specification & Value \\ \hline
\rowstyle{}
Frame Type            & Quad-Rotor           \\ \hline
Frame                 & Tarot FY650          \\ \hline
Flight Controller     & 3DR Pixhawk          \\ \hline
Motors x 4            & T-Motor MN4006       \\ \hline
Motor Controllers x 4 & X-Rotor 40A OPTO     \\ \hline
Propellers x 4        & Tarot 1555 CF        \\ \hline
Batteries x 2         & Multistar 6S 5200mAh \\ \hline
Weight                & 2942 g               \\ \hline
\end{tabular}
\caption{Vehicle Specifications}
\label{table:vehicle-specs}
\end{table}

\begin{table}[]
\centering
\begin{tabular}{l^l}
\hline
\rowstyle{}
Primary GPS         & Piksi v2.3.1      \\ \hline
Primary GPS Firmware & v0.21 (STM) v0.16 (NAP)      \\ \hline
Secondary GPS       & U-Blox NEO 7N      \\ \hline
Primary Antenna     & Tallysman TW2412   \\ \hline
Secondary Antenna   & Taoglas gp.1575    \\ \hline
\end{tabular}
\caption{Vehicle GPS Specifications}
\label{table:gps}
\end{table}

\section{Method}
\label{sec:method}

\begin{table}[]
\centering
\begin{tabular}{l ^ l}
\hline
\rowstyle{\bfseries}
Specification & Value \\ \hline
\rowstyle{}
Camera                                                                & Sony Nex-5T        \\ \hline
Lens                                                                  & Sony SEL-20F28 (20mm)
\\ \hline
\begin{tabular}[c]{@{}l@{}}Weight\\ (with vehicle mount)\end{tabular} & 424 g              \\ \hline
Sensor                                                                & 16 MP: 4912 x 3264 \\ \hline
Hot shoe adapter \begin{tabular}[c]{@{}l@{}}Fotasy SANEX Hot Shoe Adapter \\(ASIN:
B00DE4T4E2)\end{tabular}  \\ \hline
\end{tabular}
\caption{Camera Specifications}
\label{cameraspecs}
\end{table}

\subsection{Site Selection and Ground Control}
A site was selected that combined high detail features such as structures and roadways with low detail features
grassland.  A series of 8 ground control points (GCPs) were surveyed using a Piksi receivers and a base station located
at a USGS survey marker about 2.2 Kilometers away.  One ground control point ended up outside of the survey area.
See figure \ref{fig:flightplan} for more information.

\begin{figure}
\centering
\includegraphics[width=5in]{images/flight_plan_gcp.png}
\caption{Flight plan visualization}
\label{fig:flightplan}
\end{figure}

\subsection{Mission Planning and Camera Configuration}
When conducting a surveying mission, it is very important to configure the vehicle, camera, and
flight parameters. To set flight parameters and other surveying parameters, Mission Planner GCS was
used. Mission Planner provided flight status during the mission and tools to convert user defined
surveying parameters (ground sampling distance, overlap, sidelap, area of interest, flight time and
camera direction) into an autonomous mission for Pixhawk.

The mission analyzed in this paper was designed with UAV surveying standards in mind. With a 75
percent overlap and 60 percent sidelap, the vehicle flew for approximately 22 minutes to capture
218 images. The copter in this mission was flown at 20m altitude which in theory gives a 2.37 mm
ground sampling distance (GSD). Pix4D reported an average GSD of 4.9 mm due to the terrain change and altitude 
variations of
the vehicle during the mission. Figure \ref{fig:flightplan} shows the flight plan of the copter and the
locations of the ground control points.

To get the best quality possible, it was also necessary to configure the camera settings
appropriately. In order to avoid blur and compensate for the vibration and continuous movement of the vehicle
a constant shutter speed of 1/1250 sec was selected. The aperture settings and ISO were
selected automatically by the camera. This resulted in sharp and detailed images.

\section{Post-Processing Techniques}
\label{sec:postprocessing}
Post processing tools were developed in house for this project. Images captures from the camera
were not individually tagged. Instead, a file with the image names, Piksi geolocation coordinates,
orientation data (omega/phi/kappa) and accuracy (default of 5m horizontal and 10m
vertical) was generated. This file was consumed by Pix4D with the aerial images.

Figure \ref{postprocess} shows the basic layout of the post-processing routines as to allow the
methods to be reproduced.  Georeference-process.py is a top-level python script that processes the
data and generates a .csv file with image geolocations that can be consumed by Pix4d. There are
other scripts within that carry out individual tasks. Mavlink-decode.py extracts the piksi log file
(SBP JSON) from the dataflash BIN log file created by the Pixhawk. Interpolate-event.py linearly
interpolates the position data at the trigger points using the SBP log file. Query-mavlink.py is
used to interpolate attitude data at each shutter time. This attitude data is converted from aircraft Euler
angles to the "Image coordinate system" (omega/phi/kappa) specified by Pix4d in a Georeference-process.py
subroutine. All this data is then compiled into a csv file formate specified by Pix4d
with a Geolocation and camera attitude for each image.  All scripts are open-source and
available from Swift Navigation's Github repositories \url{http://github.com/swift-nav}
\subsection{Photogrammetry parameters}
In order to compare and analyze results from Pix4D, a total of 6 variations of settings and data
were selected for rendering as described in table \ref{table:postparams}.  The calibration method
column refers to whether the "Standard" calibration method in Pix4d or the "Accurate Geolocation
and Orientation" methods were used.  According to Pix4d help documentation, the "Accurate" setting
is "Optimized for project with very accurate image geolocation and orientation. This calibration
method requires all images to be geolocated and oriented."\cite{pix4d_support1}  The Included
images column refers to which images were used for post-processing (see section \ref{sec:overlap}
for more information)
The entire parameter space was repeated for the primary GPS (Piksi) and control GPS sensor (Ublox
sensor) as to make 12 total possible post-processing parameter sets.


\begin{figure}
\includegraphics[width=7in]{images/flow_charts/uav_survey_processing_architecture.png}
\caption{Post Processing Architecture}
\label{postprocess}
\end{figure}

\begin{table}
\begin{tabular}{l ^ l ^ l ^ l ^ l ^ l} \\ \hline
\rowstyle{\bfseries}
Config & Description & Calibration Method & Included Images & GPS Sensor & Ground Control \\ \hline
\rowstyle{}
1  & Piksi RTK Std             & Standard  & All               & Piksi RTK (Fixed) & None \\ \hline
2  & Piksi RTK Std low sidelap & Standard  & Every Other Line  & Piksi RTK (Fixed) & None   \\
\hline
3  & Piksi RTK Std low overlap & Standard  & Every Other Image & Piksi RTK (Fixed) & None  \\ \hline
4  & Piksi RTK Std GCP         & Accurate  & All               & Piksi RTK (Fixed) & 7 GCPs \\
\hline
5  & Piksi RTK Acc             & Accurate  & All Images        & Piksi RTK (Fixed) & Nonoe \\ \hline
6  & Piksi RTK Acc GCP         & Accurate  & All               & Piksi RTK (Fixed) & 7 GCPs \\
\hline
7  & Ublox Std                 & Standard  & All               & Ublox             & None   \\
\hline
8  & Ublox Std low sidelap     & Standard  & Every Other Line  & Ublox             & None \\ \hline
9  & Ublox Std low overlap     & Standard  & Every Other Image & Ublox             & None  \\ \hline
10 & Ublox Std GCP             & Standard  & All               & Ublox             & 7 GCPs \\
\hline
11 & Ublox Acc                 & Accurate  & All               & Ublox             & None \\ \hline
12 & Ublox Acc GCP             & Accurate  & All               & Ublox             & 7 GCPs \\
\hline
\end{tabular}
\caption{Post processing parameterization}
\label{table:postparams}
\end{table}

\section{Results}
The survey mission and the various post-processing techniques provided typical UAV survey results.  Most images were 
calibrated and stitched together to create survey outputs such as orthomosaics, digital elevation models, and point 
clouds.  In the section that follows, the resultant outputs are analyzed as to understand how improved GPS accuracy 
ground control, and different post-processing strategies can affect their quality.
\begin{figure}
\centering
\renewcommand*\thesubfigure{\arabic{subfigure}}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/p.png}
  \caption{Piksi all images}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/p_every_other_line.png}
  \caption{Piksi every other line}
  \label{fig:sub2}
\end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/p_every_other_image.png}
  \caption{Piksi Every Other Image}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/p_gcp.png}
  \caption{Piksi with GCPs}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/p_accurate.png}
  \caption{Piksi Accuracy }
  \label{fig:sub2}
\end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/p_gcp_accurate.png}
  \caption{Piksi Accuracy with GCPs}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/ublox.png}
  \caption{Ublox All Images}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/ublox_every_other_line.png}
  \caption{Ublox Every Other Line}
  \label{fig:sub2}
\end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/ublox_every_other_image.png}
  \caption{Ublox Every Other Image}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/orthomosaics/ublox_gcp.png}
  \caption{Ublox with GCPs}
  \label{fig:sub1}
\end{subfigure}%
\caption{Orthomosaics of Different Configuration Renderings}
\label{ortho}
\end{figure}


\begin{figure}
\centering
\renewcommand*\thesubfigure{\arabic{subfigure}}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/p.png}
  \caption{Piksi all images}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/p_every_other_line.png}
  \caption{Piksi every other line}
  \label{fig:sub2}
\end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/p_every_other_image.png}
  \caption{Piksi Every Other Image}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/p_gcp.png}
  \caption{Piksi with GCPs}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/p_accurate.png}
  \caption{Piksi Accuracy }
  \label{fig:sub2}
\end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/p_gcp_accurate.png}
  \caption{Piksi Accuracy with GCPs}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/ublox.png}
  \caption{Ublox All Images}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/ublox_every_other_line.png}
  \caption{Ublox Every Other Line}
  \label{fig:sub2}
\end{subfigure}
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/ublox_every_other_image.png}
  \caption{Ublox Every Other Image}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.33\textwidth}
  \centering
  \includegraphics[width=.72\linewidth]{images/DSM/ublox_gcp.png}
  \caption{Ublox with GCPs}
  \label{fig:sub1}
\end{subfigure}%
\caption{Digital Surface Model (DSM) of Different Configurations}
\label{dem}
\end{figure}

\subsection{Accuracy}

Accuracy in surveying data can be defined as both the relative accuracy between locations in the scene
and absolute accuracy in placing the data on earth. Ground control points (GCPs) and accurate image geotagging can help 
post-processing softwares with both aims.
In order to evaluate the ability to correctly scale an image, the distance between 2 GCP markers in
the post-processed image was used.  This measurement could be a proxy for many surveying outputs
as well as volumetric measurements.  For this analysis, the distance between ground control point
1 and ground control point 8 was used.  This distance was surveyed in advance to be 54.83
metres. There was little to no difference between the distance as calculated from the survey outputs
between any of the post-processing configurations. It is hypothesized that if geolocation information quality is within 
a threshold, Pix4D does not weigh the image geolocation data and most image scaling information comes from image 
processing.  Thus there were no clear gains from an RTK GPS sensor with respect to relative measurements in images.
The ground control points used as 3d Checkpoints were attempted to be used to evaluate the absolute positioning of 
survey outputs with respect to the earth.  However this method yielded inconclusive results that suggested problems in 
the surveyed positions of ground control points.
\subsection{Accurate Calibration Method}
A technical contact at PixD suggested using the "Accurate Geolocation and Orientation" calibration
methods in order to configure Pix4d to weigh more heavliy image geolocaiton information.
Configuration 5, 6, 11 and 12 (shaded in table \ref{table:qualityreport}) were rendered
using "Accurate Geolocation and Orientation" settings in Pix4D.  It was expected that this
calibration method would produce better orthomosaic and point
cloud results, but the results were qualitatively worse. Observing Table \ref{table:qualityreport},
both Piksi and Ublox show increased errors. The RMS errors increase to a point that configuration 11 doesn't
produce DSM or orthomosaic and configuration 12 cannot perform initial image calibration and thus are omitted from the 
results.  In Figure \ref{ortho} the orthomosaics of Piksi clearly show that the orthomosaics with the accurate 
calibration method cover less area compared to default settings.  The digital elevation models produced from the 
accurate calibration method yielded strage artifiacts.
One possible theory to explain the behavior is that the inaccuracy of the orientation data
starts to dominate in with this calibration method. Since the Pixhawk flight controller in the experiment has a
relatively low quality MEMS IMU, it cannot be expected to have highly accurate attitude
measurements during the flight.  Moreover it is possible that an error persisted in the conversion
from aircraft Euler angles to the surveying frame (omega/phi/kappa) required by Pix4d.  The exact
reason that this calibration method yielded poor results is unknown, but it is presented as a finding
to pose the question.
\begin{table}[]
\centering
\begin{tabular}{l ^ l ^ l ^ l ^ l} \hline
\rowstyle{\bfseries}
Configuration & X Error [m] & Y Error [m] & Z Error [m] & Image calibrated [percent]   \\ \hline
\rowstyle{}
1 & 0.185 & 0.136 & 0.274 & 84   \\ \hline
2 & 0.653 & 0.552 & 1.056 & 64    \\ \hline
3 & 0.847 & 1.182 & 1.202 & 59  \\ \hline
4 & 0.076 & 0.089 & 0.011 & 85    \\ \hline
\cellcolor[gray]{0.8}
5 & 0.556 & 0.619 & 0.989 & 99  \\ \hline
\cellcolor[gray]{0.8}
6 & 0.277 & 0.073 & 0.723 & 95   \\ \hline
7 & 0.202 & 0.840 & 0.563 & 82   \\ \hline
8 & 3.075 & 1.842 & 1.255 & 64   \\ \hline
9 & 2.191 & 3.028 & 1.897 & 57  \\ \hline
10 & 0.086 & 0.096 & 0.024 & 81   \\ \hline
\cellcolor[gray]{0.8}
11 & 0.500 & 0.625 & 0.959 & 99   \\ \hline
\cellcolor[gray]{0.8}
12 & \multicolumn{4}{^c}{Would Not Render} \\ \hline
\end{tabular}

Configurations with gray cells use the "accurate geolocation and orientation" calibration method
\caption{Pix4D Quality Report Error and Camera Calibration Data}
\label{table:qualityreport}
\end{table}

\subsection{Overlap/Sidelap}
\label{sec:overlap}

Surveying missions are typically flown with high overlap to yield high quality photogrammetry
results. Tools such as Pix4D are designed to favor image processing over geolocation information
due to lack of accuracy in the commonly used GNSS systems on micro UAVs. The hypothesis is that
with accurate geolocation data, the overlap percentage can be dropped without effecting the
performance.

The mission was designed to have an overlap of 75 percent and a side lap of 60 percent. 
As shown in the configuration table
\ref{table:postparams} post-processing was performed after removing lines and after ignoring
every other image. The removal of lines would effectively halve the sidelap percentage.  The
removal of images would effectively halve the overlap percentage.

Figure \ref{figure:rmserror} shows the mean RMS errors extracted from Pix4D quality reports for
various configurations.  As expected, both Piksi and Ublox experience significance increase in error
when lines and images are removed. That said, the error magnitude with Piksi's geolocation
information is smaller than that with Ublox. Additionally the number of images and the total area
that was successfully surveyed is larger when Piksi's RTK geolocation information is used to
georeference images. This analysis suggests that a more accurate GPS sensor can reduce the overlap
and sidelap necessary for successful image processing and thus increase the area that can be
surveyed in a given flight.


\begin{figure}
\centering
\begin{subfigure}{.5\textwidth}
  \centering
  \includegraphics[width=1\linewidth]{images/piksi_rms_error.png}
  \caption{Piksi}
  \label{fig:sub1}
\end{subfigure}%
\begin{subfigure}{.5\textwidth}
  \centering
  \includegraphics[width=1\linewidth]{images/ublox_rms_error.png}
  \caption{Ublox}
  \label{fig:sub2}
\end{subfigure}%
\caption{Piksi and Ublox RMS Error }
\label{figure:rmserror}
\end{figure}

\subsection{Initial accuracy estimate investigation}
One potential output to measure post-processing quality is the RMS errors reported by Pix4d
Software which come from the "Initial processing" step of the software.  Indeed, this paper, and
the marketing material of some vendors and other parts of the literature, use this image processing
output as a key metric in evaluating the quality of geolocation surveying data.

In this analysis, however, the RMS errors values seemed highly affected by the image geolocation
"accuracy" estimates are initially provided to the software by the user as input.  This outcome was
peculiar and unexplained.  This odd relationship is presented in table \ref{table:accuracy}. The
table shows three identical post-processing runs where the only difference was the accuracy
estimate.  As this accuracy estimate decreased, the image location errors as reported in the Pix4d
quality report decreased as well.  This behavior suggests that the accuracy reported by Pix4d for
geolocation information is more an artifact of post-processing than representing something
physical.  For this paper the default initial accuracies were used as control for this behavior.  As
an additional note, in certain cases when these initial accuracy values are constrained to values
below the accuracy of the sensors used for georeferenceing, the software is unable to perform
initial processing.

 % images processed  2d keypoints mean RMS Error [m] X RMS Error [m] Y RMS Error [m] Z

\begin{table}[]
\centering
\begin{tabular}{l ^ c ^ c ^ l ^ l ^c ^c ^c} \hline
\rowstyle{\bfseries}
Label & \multicolumn{2}{^c}{Initial Image Accuracy [m]}  & Images & 2d keypoints &
\multicolumn{3}{^c}{Mean RMS Error [m]} \\
&   X,Y & Z & processed (\%) & & X & Y & Z  \\ \hline
\rowstyle{}
a  &5   &10  &84  &5902  &0.184857  &0.136434  &0.273608 \\ \hline
b  &0.2 &1   &84  &5907  &0.126739  &0.097678  &0.163582 \\ \hline
c  &0.1 &0.2 &84  &5887  &0.061538  &0.06066   &0.111367 \\ \hline
\end{tabular}
\caption{Initial Accuracy Estimate Data}
\label{table:accuracy}
\end{table}

\subsection{Issues}
There were many issues and learning opportunities during data collection and analysis which are presented
to help future researchers avoid common pitfalls.  First, it is important to match flying speed and image triggering 
intervals with the imaging sensor's ability to buffer images. Part of the post processing procedure involves comparing the number
of triggered images (Mavlink CAM messages) to the number of shutters recorded (Piksi SBP MSG\_EXT\_EVENT message)s. If 
there are more trigger messages than images or recorded shutter events the mission was most likely flown too fast for 
the camera to react to shutter triggers.

Moreover, while post-processing configuration with GCP markers, few tie points were found in images including the
GCPs. Considering figure \ref{fig:flightplan} it is clear that most of the GCPs were placed on the
edge of the mission range. This made it difficult for Pix4D to accurately define and use these
points for the processing procedure.  In a future data collection effort ground control points should be placed in low 
detailed portions of the scene and/or areas with high image overlap as opposed to the edges of the scene.

One additional issue was limited understanding and closed 
nature of the Pix4D processing software. Many of the results would be more clear with a deeper understanding of the 
algorithms and heuristics in the post-processing tool. Software inputs such as the initial accuracy estimates
and orientation inputs (omega/phi/kappa) had unexplained roles in the rendering process. Different post-processing 
tools (Agisoft PhotoScan or Visual 
Surveyor) or a deeper understanding and/or partnership with Pix4D would improve future analysis work.

\section{Conclusions}
Micro aerial Vehicles have the potential to greatly reduce costs and complexity of aerial surveying and to arm 
end-users with actionable data.  To that end, this paper presented methods and results of integrating Swift 
Navigation's Piksi RTK receiver into a MAV surveying system.  In the end, it is clear that more accurate geolocation 
information can improve aerial surveying results.  Specifically, with increased geolocation accuracy sidelap and 
overlap can be reduced and the image location errors reported by image stitching software can be reduced.

\section{Acknowledgments}

Please acknowledge Andrew McIntyre from Pix4D for his technical support and Malcolm Morrison from Agriculture and 
Agri-Food Canada for his thoughts and advice.  Also please extend a special thank you to Diana Schlosser (Swift) for 
access to her property.

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\thispagestyle{lastpage}
\end{document}