I'd like to invite the diydrones members to participate in a project I started on github: Project Ion on github

The control of uav's have improved a whole lot in the past couple of years and you can now pretty much put one in the air and wait for the results to come back, whether these are videos or images. There are numerous posts and videos of people who show how they made orthomosaics, 3D models, point clouds and digital elevation models.

I started Project Ion with that motivation in mind; thinking from the perspective of your customer (or your own needs), how do you grab the orthomosaic / DEM you just created and start to answers the real questions that end users have and answer those questions in a visually compelling way?  Not every office contracts a bunch of engineers to work with ortho and DEM data.

The work above was done with a team of experienced surveyors. In this case I played a supporting role to provide them with the orthomosaic, which was for visualization purposes. They used the geo-referenced ortho themselves to validate their collected data points. Since I have my own methods to derive an accurate set of control points, you get two disjoint data sets which have no dependency on one another. Here, I'm just demonstrating how the ortho could be annotated with help of some of their survey points to better visualize the land and the boundaries. In many cases, surveyors provide an iconic diagram of the land made in autocad and they don't have acces to a recent ortho. So it makes sense to also think towards support roles for uav in surveys, where the orthomosaic is just used as a means of cross validation and visualization of the results.

Another thing that came up is that the area had recently been leveled. They were interested in the accuracy of this leveling. With a contour map, you can easily process the DEM to extract these contour lines at any interval. Of course, everything can be exported to CAD tools for further processing.

All images above were made with the help of QGIS, now starting to become a very powerful GIS application with a bunch of plugins for many different purposes.

So I'm looking for contributors to set up this knowledge base. Not everything has to be written as a tutorial on the wiki, the idea is to link to existing content, link to videos and just show what others have done, generate ideas and make it a wiki that you can browse to find really cool applications that go further than orthomosaics from pix4d/photoscan, then explain how such resuls are achieved, etc. I'm not excluding that the project develops some of its own scripts and tools to faciliate in that process.

How to become a collaborator? Send me a privmsg on diydrones if you're interested and your github username so I can add you as a collaborator on github. If you don't have a github account and want to send me a cool link to some video or tutorial on how uav data is being used or applied, feel free to send this in a privmsg as well.

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Comment by Martin on June 8, 2015 at 4:09am

That's a great idea.

My main issue has been with the large number of GCPs needed for larger areas (~10+ sq km).

In some cases this makes UAV photomapping unfeasible compared to the traditional methods where surveyors do the field work. These cases are mainly when the clients just wants an orthomosaic so they can measure the area of a field or see if the contractor has done the work they agreed upon. High accuracy is usually needed.

If I had to place 50+ GCPs on a large area then it wouldn't be any faster or cheaper than having surveyors do their measurements.

Does anybody have advice on how to reduce the number of GCPs?

Comment by Gerard Toonstra on June 8, 2015 at 4:44am

As I found out, to send priv messages, you first need to be buddies. I created a gmail account for this project:

gisprojection@gmail.com

If you wish to collaborate, send an interesting link or otherwise contact the project, send an email there. 

Comment by Gerard Toonstra on June 8, 2015 at 6:25pm

Martin,

People have been experimenting with better GPS's that have higher output frequencies, so you get better fixes where photos were taken. This does help to increase the accuracy. Another important issue is to get the time exactly right when the photo snapped, not when it was triggered. Not sure what your current accuracy is with just uav data, but this could help to bring that to half at least.

One way to deal with this is to sample their work in specific places where it matters most and just check the rest for obvious errors.

When cheap RTK comes around and actually works, even 50% of the time, then you'll probably be able to stop worrying about those GCP's.

Can you quantify this "high accuracy" ?

Comment by Martin on June 9, 2015 at 1:36am

Thank you for the advice Gerard,

I am thinking on building a camera controller of sorts that would log raw GPS data and the attitude of the camera at the moment the photo is taken. I have some ideas on how to determine the snap moment more precisely

The allowable error for area measurements was 0.01 ha per hectare. That was for a 3000 ha area.

Comment by Gerard Toonstra on June 9, 2015 at 2:52am

So 0.01ha is roughly half a meter on either side. That's a bit too small for using satellite images. QGIS for example has this openlayers and georeferencing plugin that you could look at using. Then you use the satellite images to find good natural points (fence posts, etc). However there are no accuracy statements made by Google. there's been research on higher resolution versions in cities.

http://www.qgistutorials.com/en/docs/advanced_georeferencing.html

A mix of both methods may give you what you need for now. If you can get the client to relax the constraint on some areas down to 0.05ha for your work (not that of the surveyors), then that's 5m or 2.5m on either side for each hectare and that would fall within the accuracy of this method, assuming 5m error on satellite images.

Comment by Martin on June 9, 2015 at 3:51am

I already tried using a local map which has 20 cm resolution. If the resolution is 20 cm/pix and you are off by two or three pixels in different directions then the error will creep up fast. Anyway, the result was not good enough.

Comment by Martin on June 9, 2015 at 7:09am

Hi Martin,

Yes, establishing GCPs is a pain. Especially in inaccessible areas. You can avoid it by orienting the images from top to the ground which means having accurate positions of camera perspective centers. This is not trivial from many reasons, first you have to get the antenna position, synchronize the sensors, correct the lever arm etc. I doubt that there will be cheap L1 RTK receivers, fixing the ambiguities just with one frequency is not simple, especially in high dynamics.

So you can either buy something like Mavinci Sirius Pro or senseFly eBee RTK or make your own UAV with a geodetic receiver. We have both, eBee RTK and custom copters and planes with Pixhawk and GNSS RTK and they works perfectly, GCPs never again:), just a few check points and that's it.

 

Comment by Martin on June 9, 2015 at 9:41am

Thank you for the advice Martin,

If it's not a secret what RTK receiver are you using with Pixhawk?

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