Today we're going to be using the iPhone 14 pro with the viDoc RTK Rover using the viDoc
RTK with the pix4dcatch app provides iPhones with centimeter level accuracy by utilizing
RTK corrections to all the scan data that is collected by the iPhone and so using the viDoc
and the iPhone I want to survey this city center and create a high accuracy survey grade Point
Cloud now several months ago I surveyed this exact City Center with a serving Total Station I'm going
to be comparing that data set with the iPhone and the viDoc data set this will provide us
a good analysis of the data that we collect in comparison to the total station all right now
I've loaded up the pix4dcatch app and as you can see in the live view I can come up to the
top left and connect my viDoc to my iPhone I've input all of my n-trip information for
the Michigan Department of transportation's cores Network everything looks good here so
I'll hit connect and now the rtk is fixed so any data I collect will receive rtk correction
now with the iPhone 14 pro we have an enhanced 48 megapixel camera for for higher resolution
image capture and while we can't capture the full 48 megapixels pix4dcatch does have the ability
to increase the level of resolution from a normal camera sensor if I come down on the
bottom right at the settings menu we have some settings regarding how we're holding our phone
and the angle at which we are capturing data but if you come over to the advanced settings
and you select resolution you can see now we have a normal resolution a 4K resolution and
a maximum resolution so I'm going to select maximum resolution which will provide us with
the highest level of resolution for our imagery I'll go ahead and hit done and now we can start
collecting data now I'm going to be doing two separate scans here the first one is along the
curb of the road and the second will be along the building by taking both of these data sets
and merging them together in pix4dmatic we'll be able to create a highly accurate 3D reconstruction
using both photogrammetry and lidar all right I'm going to start on this side of the building and
I'm going to start to capture first the sidewalk and then go around into the curb and I'll start
here we go we are now collecting data and as you see this on the bottom is the lidar Point Cloud
coming around the manhole and I'm going to just turn and come around this side of the curb also
get the mailboxes here we go I'm going to wall just along the curb here I'm just gonna hug this
curb here okay and I see we have a fire hydrant right here and I want to capture as much detail
this fire hydrant because I want to use it as a benchmark so I'm going to wrap myself around
here just to get a complete 3D perspective of the fire hydrant back up here get more of the curb
and we'll continue to collect curve it looks like there was a old light pole there that got knocked
down this is a catch Basin and a tree we're still maintaining our RTK fix which is good here we go
I'm just going to keep walking along the curb here the most important thing here is getting
all the features that are along the curve like the trees these benches and as well as getting
the curb itself so I can get the elevations of it later we got some manholes here so I'm going to
get the manholes in this make sure I keep getting the curb though there we go I can grab this tree
we're almost done here with the curb and I'm going to come up here to just hug the side of
the building we'll end it there and we will save the project and if we look here we can see a very
nice 3D Point Cloud of the lidar sensor capturing the curb we've got 933 images so I can go through
all of these images here and we've got every little bit of the curve very nice I like that
all right now I want to take a second scan around the buildings to capture the details of each of
the buildings getting building Corners down spots and existing finished floor elevations all right
our rtk is fixed I'm going to start right here and let's start collecting data and we'll just walk
here just along the building maintaining our fixed reading as you can see I've got the down spots all
the finished floors here looks like somebody got a package I'm going to scan their package
okay we're nearing the end here all right and I'm going to do just a little bit of
the sidewalk just to match with our last scan all right and now we can save our scan and looking at
the building scan you can see we've got every single door a nice amount of detail that we
can work with and we'll definitely be able to extract the finished floor elevations for all
of these units I really like how this point cloud looks let's take a look at the images real quick
here we go here are all the images all 629 of them very very nice all right now that we've finished
collecting data with the iPhone 14 pro with the viDoc rtk Rover let's process all this data
so we can take a look at the accuracy and see how it all turned out hello and welcome now I
brought in all of my data using the SanDisk lightning to USB flash drive if you like to
purchase this flash drive from Amazon check out the link in the description you can also
upload the data to Google Drive or iCloud and access it on your computer so I've got both of
my data sets right here they're both zip files and when I open up the files I can see all of
the imagery and if I click on one of these images for example let's just say this one
we can see just the resolution is incredible I mean look at how much detail we have and that just
shows how much more detail pix4dmatic has to work with with higher resolution imagery both
of these projects come with their own respective lidar Point cloud in a PLY format and all these
data will be merged together to create one large data set now I've opened up pix4dmatic and I'm
going to add both data sets into the software I'll just drag and drop so that's going to upload
all of the imagery to this project a few moments later okay there we go and it was able to identify
the location of all the imagery I'm going to now identify the coordinate system so I am going to be
in Michigan South and ad83 vertical coordinate system and a vd88 and in geoid 18. apply now
usually I always encourage everybody to use ground control points ground control points are
the validation that you need to have accurate data but because this is an experiment and I want to
analyze the raw data that comes out of the viDoc RTK I'm not going to add any ground control points
instead I'll be adding checkpoints after I process all the data so I'm going to come up
to the top here to the processing options menu and already I've got the pix4dcatch template loaded
and I'm going to recommend you just stick to the recommended template from pix4d that's going to
provide you with the most optimized settings for your output so I'll just select calibrate and I'm
going to leave this the way it is and I'm also going to select the depth Point Cloud the dense
Point Cloud this will generate my photogrammetric point cloud from the imagery I'll turn on the
sky filter why not and then there's the depth and dense fuse which will fuse both the lidar and the
photogrammetry data sets together I also like to process a mesh it's just nice to look at sometimes
clients enjoy looking at it everything looks good here so I'm gonna start processing and we'll be
right back when everything is done 336 minutes later all right all of the data has been processed
let's take a look at it and see how it turned out as you can see the green imagery over the
blue imagery is calibrated locations so there's a slight shift as always to make everything
calibrated and fit correctly and now let's switch over to the 3D View and look at that we have a
nice Point Cloud now this is the fused Point cloud with both photogrammetry and lidar let me show you
what the photogrammetry point cloud looks like as you can see it looks pretty similar there are
a few holes and gaps especially maybe around the trees and this is what the lidar point cloud looks
like now this point cloud looks a lot more scarce but that's just because there's less points to
work with because everything was directly measured from the lidar sensor however this point cloud is
very useful because it'll give us data on points that we're unable to reconstruct using imagery so
if using both of these Point clouds together will give us the most optimal data set to work with
and that's where this fused Point Cloud comes into play things came out really really nice everything
came out crisp you see the base of the mailbox really nicely corner of the building is very
easily defined you can literally see the bolts on this fire hydrant how incredible is that some
noise around the branches what that's expected the light pole came out very crisp and clean a
little bit of noise but not too bad and this right here is the mesh as you can see the mesh is always
going to be a little funky on the outsides but if we come to the curb here we can see how clean this
is very crisp and presentable the fire hydrant has been reconstructed and it actually looks
incredibly good and while I may not use the mesh file to do any kind of data extraction it's always
just nice to look at but I'm gonna go ahead and turn it off and stick to just the fused Point
Cloud now to test the accuracy I have reference data that I collected with a surveying Total
Station and I tried to take points along cracks and seams on the curb just so that it's a little
easier to geo-reference the true positions and I set these all to checkpoints as to not influence
the raw data and here we go we have all of the checkpoints brought in the total station points
are the gray points and the geo-referenced locations are the green points I'm going to
start by showing you the best points the points that had the least amount of error and then show
you the worst points the points that had the most amount of error starting things off on the south
end of the project we have this shot it looks like it's a spot where concrete meets a curb we've got
about seven hundredths of a foot in error in the easting right on in the northing at about 700ths
in the elevation and for my metric users that's about two centimeters in X right on in y and about
two centimeters in elevation that's really really good for some iPhone data that is being corrected
with the viDoc RTK Rover another point I want to show you is the existing finish floor of this
unit you can see where we shot in and where we geo-referenced our checkpoint we've got an error
of about one-tenth in the easting right on in the northing at about four hundredths in the elevation
so that is about three and a half centimeters in X again right on in the northing so right on in y
but it's only about a centimeter in the elevation we think about it we don't really care about the X
and Y value we mainly care about the elevation when it comes to existing finished floors of
houses or of condo units like this one so it'll be off about four tenths or one centimeter is
not that bad it's actually really impressive going down here to the North End of the project we have
this curved shot where the curve and the sidewalk and the brick all meet at the same time and we've
got some differences about 500ths in the easting again right on in the northing and about one and
a half tenths in the elevation so it's again one to one and a half centimeters in the X right on in
the Y but it's about five centimeters in elevation now five centimeters is a little bit of error um I
would say if you were to introduce some ground control points maybe you can drop that down a
little bit but ultimately it's uh not horrible and in many cases that is pretty acceptable accuracies
some of the more challenged areas I would say back here in the north and I got about two tenths
in x one tenth in y and about 15 hundredths in elevation which is about three to six centimeters
now there are certain points that I actually would prefer to use use an iPhone with the viDoc RTK
rather than using a total station because the total station just isn't as accurate at finding
these points one of these would be trees it's very difficult to find the location of a tree as you
probably use offsets to estimate the location of the tree which is not the most accurate
method definitely not as accurate as measuring directly to the tree take for example this point
right here I've actually referenced where the tree is in comparison to what I have with the
total station which is in blue here our arrows are about four tenths in easting and northing
and two tenths in elevation you're talking 6 to 12 centimeters of air so I don't see what even
call this error I'd call this differences because again I think the total station is less accurate
in this instance and the iPhone is actually more accurate because it's giving a direct measurement
to the tree another example of this would be the mailboxes I kind of just held the mailboxes close
and tilted my rod I was quite a bit off here and yeah even the elevation is off because I tilted
the rod so again the total station is not going to be as accurate with these points which is why
we have so much error in comparison to the iPhone with the volume on now what about the Benchmark
elevation on the fire hydrant how did that turn out you can see here the arrow on the fire engines
easily identifiable in the imagery we've got about six hundredths in easting 1500s in the northing
and only three hundredths in the elevation one centimeter of error in the elevation for
a benchmark is not bad and that is the accuracy you can expect using the iPhone 14 pro with the
viDoc RTK Rover if you guys would like to learn more about this GNSS receiver check out my link in
the description and visit pix4d's website if you like learning more about surveying and geospatial
Technology then be sure to subscribe to my YouTube channel and I will see you guys next time