One of the things we have been working on this summer is bringing online the fixed wing sUAS, variously known as The Catfish, Mr. Chow, Local Hawk, or simply "the fixie". It is a foray into more travelled ground for sUAS in general than DeaconEye, but it brings with it a new set of problems for image analysis. New cameras to be triggered, and new ways that imagery varies among missions to deal with. One of the things that is very clearly different is that the fixie doesn't "mow the lawn" like a copter. Turns put the un-gimbaled camera out of angle enough that we're not mowing, we're farming. And when you're sowing, your turns at the end of the field are out of bounds--you use them to get the machinery turned around for the straight rows--and then you come back and plant the edges. That's what we need to do with Local Hawk. Set the straight parts of the flight plan to capture the imagery, and make the turns out of the desired scenes.
More generally, it'd be nice to start a set of python scripts for our image workflow that looks at the EXIF information and automatically leaves out pictures beyond a predefined bounding box for the imagery, and, given some of the severe cross-tracking in high winds and roll during gusts, takes out images with high yaw
Wednesday, June 26, 2013
Saturday, June 22, 2013
Making the imagery accessible
So in the time that we've been offline we've increased our stable to include ArduCopter and ArduPlane, and climbed the learning curve on the minutiae of MikroKopter to the point where we have it flying autonomously to take high resolution canopy mosaics and generating DEMs of canopy surfaces. All very cool.
But this generates enormous amounts of data, and we have to be able to process it and display it efficiently. Not only in ways that are good for us in terms of the science goals, but also in ways that people can use on the web.
So how to do it?
One of the ways that we are all familiar with that uses tremendous images in a friendly way is through Google Maps and Google Earth. And the Google Map API can be used to serve and navigate our own images. Let's take a large mosaic--in this case a section of Long Caye in Belize--and
But this generates enormous amounts of data, and we have to be able to process it and display it efficiently. Not only in ways that are good for us in terms of the science goals, but also in ways that people can use on the web.
So how to do it?
One of the ways that we are all familiar with that uses tremendous images in a friendly way is through Google Maps and Google Earth. And the Google Map API can be used to serve and navigate our own images. Let's take a large mosaic--in this case a section of Long Caye in Belize--and
Friday, June 14, 2013
4WD Mowing
I knew it would be an interesting day of flying when you have to put it in 4WD to get to the flying site. Four huge Poplars across the road diverted us out to the Yadkin river and back.
After a Deans soldering marathon and Max putting in a lot of work to ready the copter we loading up and moved out. Max had us mowing the lawn again. We imaged about 7 ac in no time as a test. We learned the new 12v BEC interferes with the compass and we need to either move or shield it. We also learned the copter uses about 500 mAhr/min. So every 1 Ahr gives us about 2 min of flight. Oh and MAXOsnapper worked perfectly.
The Arduplane, now called "Catfish" was a little terrifying. It was not set up for the wind and it struggled to make way points. We almost lost it the trees but Max saved our bacon and yelled "Put it in manual". We have some work to do on speeding the camera up as well. Max got some stick time on my little home built Tricopter and the Arduplane. He had a real nice save on th plane.
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