Now let's factor in the ground control points to bring an absolute accuracy into the map.
I will go to project and click on GCP / MTP manager.
Here, you can either import the GCPs in an excel sheet or add each point manually.
Let's do this manually.
So I click on "Add Point".
Now I know that my first point is "C".
So, I name the label as �C�. The point type is chosen as 3D GCP.
I switch over to the Excel file in which I have the values of the GCP from the Trimble Business Center.
First, let�s copy the Northing value for the point �C�. This corresponds to the X value.
So I paste it here.
Similarly the Easting value gets pasted into the Y column.
And the elevation value gets pasted into the Z column.
I will leave the accuracy of the captured points as the default value of 0.02 meters.
Now, let's add the next point.
It�s �R1� in my list,
so I'll go ahead and make an entry for it.
Again, It's a 3D GCP.
I�ve got the Northing, the Easting and the Elevation all copied in.
Let's skip over to the end of the last entry.
Now, we need to tell the software where exactly these coordinates are on the map by marking each on
at least two images.
There are two ways to do this - Using the rayCloud editor or the Basic Editor.
Since, we have performed Step 1 already, we will use the rayCloud editor.
Now, the GCPs could have been located on the images using the Basic Editor before running step 1,
but performing this step after Step 1 using the rayCloud editor enables faster and more precise processing.
So, I go ahead and click on it.
We find that the points are listed under GCPs/MTPs in the layers panel.
Let�s click on the point �C� and start the process.
Now, you have the panel displaying all the images that have the �C� coordinate within them.
These blue dots that represent the location of the coordinate value in each image.
You can clearly see that each image has a different location for the same coordinate because of the
inaccuracy of the geocoordinates captured with each image.
Now, the idea is to tell the software where the �C� coordinate should actually fall.
And we do that by marking it on at least two images. So, let's begin.
I�ll start with this image. Drag it to the centre of the �X� and zoom in. And click
on the center point.
Now I'll do this for the second image as well.
Zoom in as much as possible and click on the center.
Let's click on apply and do this for a couple of more images.
You can see that Pix4D has tried to locate the exact point on this image
after clicking apply.
Let�s refine it further.
There, I have clicked it. I�ll click apply again.
Now let me go over the entire set of images.
I can see that the point hasn't been identified well enough in this image. So I'll mark this as well.
Click Apply again.
I do this a couple of more times.
Now let's click on "Automatic Marking".
Pix4Dmapper will search for automatic color correlation of the clicked pixel on the rest of the images.
Thus the position of the GCP will be optimized in more than the clicked images
if the color correlation is good.
I'll go through the image list again just to be sure. Looks good enough!
I�ll click on apply one last time and now it shows me that 40 images have been marked.
I've already done this step for "R2".
Let's repeat the process for "R1" now.
I'm going to fast forward through the process. and I'm going to skip through to the end of marking "L2"
as well.
Now, we need to reoptimize Step 1 processing to factor in the GCPs.
So, click on "Process" and click on the "Reoptimize". It gives you a warning that the results of Step 1 will
be overwritten. Absolutely no problems with that and the process begins.
We wait for the process to complete.
And now it's completed.
Now, let's regenerate the quality report to make sure that the GCPs got factored into the processing.
So click on process and click on "generate quality report". The quality report gets generated.
It shows you that the 5 GCPs have been processed and the RMS error is 0.041 meters, which is
essentially 4.1 centimeters.
Pretty good isn't it!
Here's a more detailed breakdown for each GCP.
Now, in the absolute geolocation variance, the Root Mean Square (RMS) error is the error between the initial and
the computer image positions.
You can see a huge shift in all 3 axes - a 2.1 meters shift in the X axis, almost a meter
in the Y axis and a 9.23 meters in the Z axis!
Now that's the kind of difference that GCPs make in the project's absolute accuracy.