OpenDroneMap – Simple Images into 3D

We are using ODM for generating orthomosaic photos and it produced better results than the commercial offerings we have seen (In some cases MUCH better). Note this did require us to tweak the settings some but overall it was pretty easy process.

Things of Note: 1) ODM didn't quite scale as well to very large number of photos. (I think this may have been fixed now). 2) Our use case was pretty brutal. ~1 cm resolution photos of a mostly uniform row crop fields. Some of the other tools were really bad at not rotating the rows 90 deg in parts of the field. 3) I was in on the POC and selection process but not the production runs so I'm not sure how things have changed over the last year. 4) Some of the comparisons where with different photos (Looking at a custom full path) 5) We didn't look at 3d much at all, but it was pretty cool to be able to see 3d tractor tire marks across and freshly planted field. The 3d quality would really fall off at the edges when there was less overlap. I think it was only good in relative hight as well but our tests were very flat.

I used both ODM and Drone deploy and was amazed at the results that Drone deploy gave me. ODM results were unfortunately poorer (the generated models were far less detailed) plus it took a loong time to analyze few hundred pictures on my laptop, while Drone deploy model was ready pretty quickly. So while I love good open source software, it feels that ODM has still long way to go.

At work, we tried DroneDeploy and Agisoft, but we settled with Pix4D, expensive, but we got better accuracy and the people who operate the drones found it easier to use.

I was thinking the same thing about OpenAerialMap https://openaerialmap.org/

That's a really roundabout way to do that though... He's basically taking free height data, converting it to a raster image, and then approximating it in the editor (which tries to convert the raster heightmap back to a proper map). The actual satellite imagery might be harder to (legally) get (and he basically suggests removing the copyright notices in the video).

It's cool that it works, but it's also the "wrong" way to go about it considering all the tools around to work with open, free GIS data.

Google Earth Pro is now free, and has an export high res image function

I'm familiar only with the DJI drones and they do capture Pitch, Yaw and Gimbal and a few other variables as well. It stores them on the "extended" part of the EXIF.

Depends on the drones, but professional photogrammetry drones certainly do. You often have position (latitude, longitude and altitude in WGS-84), attitude (yaw, pitch, roll) which already contains the gimbal orientation (e.g. the drone applies the lever arm to go from the IMU position to the camera optical center and output those) along with their covariances (accuracy) as well as timestamping. If the camera has a rolling shutter, you might even have imu data (i.e. accelerations) to correct distortions. Another important data for the reconstruction is the focal length.

Ultimately, it is the job of the reconstruction software to use those data. Usually, they use them to initialise the process, and use the covariances as constraints, then they run their optimiser based on that. If your accuracies were too tight, the reconstruction will often fail as they trust you on the accuracies. However, the better/more accurate the data (e.g. RTK data), the faster/easier it is for the software to do its job.

All ODM needs is a centroid Lat/Lon from the EXIF tags on each photo. You can optionally add a text file that gives a pixel an exact Lat/Lon. From this it matches features from the photos to calculate the exact pitch, yaw, direction. I think you can specify those in the EXIF but it is really hard (i.e. expensive) to get these to high enough quality to render the final image.

In our case the high quality gimbals where more than our drones. (~$15k each)

OpenDroneMap use a text file where you specify image name and pixels with their corresponding GPS coordinates for those pixels. Pitch, yaw, gimbal direction can probably help you with optimizing those algorithm. I am thinking of using OpenCV for image recognition of a large “chess board”, from there it should be easy to get both the GPS-coordinates and pixels in the images seeing this calibration board.

I don't think it uses GPS for that. You can match two partially overlapping images. From the movement of the feature points in the overlapping areas you can calculate your camera orientation, and thus the 3d point cloud of feature points ( or something close to this ). I think it is called structure from movement, the readme links a paper, maybe it's worth reading for you.

In the lab I work in we have internal tools for doing something like this for precision agriculture, but I have never heard of an open source solution. UAVs generally don't record their orientation or GPS data without setting up a separate log file (I have no idea how you do this on Pixhawks. We use BYU's autopilot or our own autopilot).

Maple - https://3d.bk.tudelft.nl/liangliang/software.html

PolyFit - https://3d.bk.tudelft.nl/liangliang/publications/2017/polyfi...

The better the photograph, the better the output quality. Photogrammetric output will always have noise. Some materials don't really contain any surface details that the algorithms could use to attach feature points to, so they will be blank. Large white walls and large windows are especially difficult.

Don't crop the photos. Prefer slight underexposition. For each detail you want to capture, have it visible at least in three photos. Capture photos from different position (ie. rather move yourself than just turn the camera) with at least 30% - 50% overlap between photos.

You can find lots of tutorial in the internet. Search term that hits gold is 'photogrammetry tutorial'. For example:

https://medium.com/realities-io/getting-started-with-photogr...

If you just want to try out the technique, I suggest you download the free test version of Zephyr 3D. You can just give it a bunch of photos and it does a fairly sane ops with the default options.

https://www.3dflow.net/3df-zephyr-pro-3d-models-from-photos/

If you like it, you can start experimenting with free and commercial tools including the one in the topic of this thread (see my other post in this thread for some other photogrammetric tools).

If you have a large set of photos you will start to need a fairly beefy computer with tens of gigs of RAM the least. But, you can get pretty nice results for e.g. just a single house with just a regular modern desktop PC.

I love the idea of your proposed application. However, I don’t see it as a problem needing solving. It’s fairly easy to survey a solar installation and potential shadows using basic trigonometry and sun charts. I would imagine many solar installers can do this in under an hour.

I was running some experiments with Drone Deploy and my DJI Mavic to document road damage and flooding in my neighborhood because the city has neglected to maintain our storm water infrastructure. Perhaps I wasn’t using high enough overlap but I found I could not get quality orthophotos at the desired resolution because of too many trees. Using a higher altitude (lower resolution) would yield quality orthophotos but then the details I wanted to see like potholes and standing water were not clear enough. My point is I think these tools can work really well when the conditions are right, as evidenced by all of the high quality samples and demos, but there are still a lot of situations where you’re just not going to get very high quality due to conditions of the subject area.

I would say the quality is relative to your input and to what type of application you need results for.

If you are gonna use the pointcloud as a cheaper and faster alternative to LIDAR and need to take measures with centimeter precision, you will need a lot of overlap (we use 75%) and the drone to take the photos automatically to be sure it covers most of the desired area. I would also recommend a commercial tool for the task. We have jobs that take one week to process with state of the art hardware.

But if you just want to do it for fun, you won't need to get this complicated and can do even with your phone.

FWIW, ODM was slightly better quality than Photoscan in our tests. We only tested orthomosaics but ODM had less error to our ground control points (note we didn't feed the givens into these tests) and less aberrations.

The ZED camera works by using two high-resolution RGB camera, finding matching points between the two, and using that to calculate depth. Because they are using high-resolution RGB cameras, they can generate very high resolution depth maps. However, this is also their main drawback; with just RGB images, depth map calculation will fail on texture-poor surfaces, such as plain white walls, because it's very difficult to match points.

They are fundamentally performing photogrammetry with two cameras at a fixed distance.