Making maps with the help of small UAVs seems to be easy enough. However, when geometric accuracy is critical, how do you determine and state the estimated accuracy of your mapping product? One way is to establish a test bed with a redundancy of accurately surveyed check points which are used as independent bench marks against which the map can be compared. In this picture we show a comparison between high accuracy GPS derived check point coordinates and corresponding coordinates as derived from our 3D model. Note that for illustration purposes the horizontal errors were scaled by a factor of 1000.

The 3D model was created from 261 aerial images (13mm GSD) captured by a Sony a6000 camera with 16mm fixed lens. The camera was carried by a Pixhawk equipped modified Steadidrone quadcopter. The flying time was 10 minutes and, using our V-Map system ( ), we could accurately survey the 12 ground control points in about 15 minutes.

Using 105 check points in our error analysis we can now confidently claim that when we map similar terrain with this particular method we can realistically expect to achieve the following accuracy:

Horizontal approximate circular error at 95% confidence 0.028m
Vertical Accuracy at 95% confidence: 0.039m

Not bad, especially when considering that the 4.75ha map can be delivered the very next day after image acquisition.


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Comment by me on February 19, 2015 at 9:10pm

Very interesting. Have you run any tests in tall grass or another non-hard landcover? You essentially tested absolute accuracy of the system but my guess is that both horizontal and vertical accuracy radically decreases in a tall vegetation. I've done a lot of research and my thesis looking at LIDAR accuracy in tidal wetlands and even with lasers we struggle to find the ground surface.
Comment by Rami Kopelman on February 25, 2015 at 1:46pm
How do you set the A6000 focus during missions?
Comment by SkySight on April 1, 2015 at 1:26pm

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Comment by Jim Peterson on April 14, 2018 at 5:39pm

As always, very nice work Walter! 


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