We did a quick evaluation how much accuracy we could achieve on all axises using a multirotor. We read many accuracy reports from fixed wings and this teaches us that the planimetric accuracy (horizontal) is usually about 1x the ground sampling distance (GSD) (0.5 if you're really good and have better cameras) and that the vertical accuracy (Z) is usually 2-3 times the horizontal accuracy. That's only valid for some altitude ranges, the regular flight altitude for uav's between 80-150 meters. Forward velocity and trigger distance requires a certain altitude to make it work.
Here we lowered the altitude from 80m to 40m and used a multirotor. We wanted to find out whether the vertical accuracy definitely would improve and hopefully establish a 1:1 relationship between vertical accuracy and GSD as well. The reason why vertical accuracy would improve steadily is because there's more perspective in images at lower altitude, so you pick up more height information in each image, which corresponds to better Z estimates.
In this example case we flew at 45 meters with a hexa at a speed of 3 m/s to get a high 85% forward overlap, making it more difficult for a wing to do the same. 211 photos were taken. The GSD produced is 1.41cm.
The photos were georeferenced using 5 marker points that were collected with high precision GPS equipment. The expectation is that when these GCP's are marked in the image, there's about a 0.5-1 pixel deviation, so it's expected that the error in marking GCP's is about 0,5-1 GSD as well. Sharper pictures and better markers reduce that error.
In this case we had 2 less accurate GCP's, so the planimetric accuracy of this dataset eventually became 1.7cm, slightly above 1* GSD. What we confirmed though is that we got a 1.8cm vertical accuracy for this set, (or rather, the residual error from the mathematical model).
This dataset could have been improved as follows:
- Better marking of GCP's and more attention paid during GCP marking.
- Sharper photos (better lenses).
- Higher precision GPS.
In the end, the maximum accuracy that one should expect with this equipment is 1* the GSD and better equipment isn't going to make this magically happen. This accuracy isn't correlated to the real world, that would be a totally different exercise altogether.
Here are some detailed images of the point cloud from close up. Notice the vertical detail of the irregular curb.
And the detail of a house. The planar surfaces aren't warped, a good indication of excellent georeferencing and accurate point triangulation.
This experiment is very relevant, because Lidar is commonly used for "real" precision projects, often considered for work where they need better than x cm precision. Although lidar data is probably as accurate as 5mm, it is also subject to occlusions and the station needs to be moved around a lot to get proper point cloud coverage, so operating it isn't all that easy.
UAV point clouds may always have less accuracy than laser clouds, but they do have the advantage of the bird's eye view: they generate data across an entire region with the same consistency in accuracy and density, whereas lidar isn't that consistently dense for every part of the survey area due to occlusions.
Price makes a big difference too. Lidar stations apparently cost $175k to acquire, whereas uav's probably put you back by $3000. The question that one needs to answer is whether the slight improvement in accuracy is worth the extra money.
What this experiment shows is that also for uav's 2cm vertical accuracy is probably within the possibilities, pending further experiments where datasets are compared against points in the real world.
I think getting 1.4cm error with pictures shot from 45m above is a pretty good result. My own experimentations let me "only" obtain an average 3d error of 2.8cm from an average altitude of 15m, using a 20mpx 1'' compact camera using Photoscan. But at least one exercise ended with 1cm error from 14m alttude.
I am getting into Ortho and Point cloud and I have been extremely impressed with the technologies now available. I have put together a EasyStick with a pixhawk for this task.
Looking forward to finding out what I can do....
When I was attempting this with my Hex (Balanced and low vibration) I was getting alot of blurry pic. How did you mount your cam to limit vibration?
This is a feasibility evaluation for a large project. The software used was pix4d (desktop version). That really is very good and gives options to control smoothing and filtering of points.
The camera is a simple point and shoot (IXUS 230HS), but any reasonable P&S that people talk about here should do. I live in Brazil and we just had a very favourable day with 4/8 cloud cover. The sun here is so strong that in bright daylight it's very likely to overexpose, losing all features. The camera has custom exposure control. Control over source material is the most important thing here.
The area is 200x140m. The flight took around 10 minutes. Processing was done overnight, but the software gives you different options for processing that can reduce processing time by a factor 4-5.
The autopilot used was apm 2.6+, performing flawlessly, 100% autonomous flight, takeoff->landing and perfect control over the camera trigger.
Hi Gerard, a couple of questions please:
1. What software are you using for your Point Cloud Model generation?
2. What camera are you using?
3. How big is the area that you surveyed and how long did it take you?