Today we release the first beta version of our application to view and analyze point clouds in Virtual Reality. We have named the application PointCloud XR and you can read more about the development and download PointCloud XR. Please add a comment on our Facebook page about your thoughts after you have tried it!
In the current version (2018-12-14) you can do the following:
Open points in LAS 1.2 format.
Depending on your hardware you can open about 15 million points and move around fluently.
You can “fly” around in the point cloud using your hand controller.
Change the size of the points.
Color the points by RGB, Class, Height or Intensity. The intensity can alway be blended with either of the other coloring modes.
Measure distance. You can snap to points and restrict measurement in y-direction or in the xz-plane.
Select points using a sphere. The selected points can the be removed.
Save the edited point cloud in PCXR-format. LAS exporter is not implemented yet but we are working on that.
Set a new start position. This will only work with a file saved in PCXR-format.
Change the throttle (how fast you are flying when you press the trigger).
Here is a clip when a few students try an earlier version of the application.
Now we continue with the second half of the course. The first part gave an overview of drone systems and regulations as well as drone flying and flight planning. Now we continue with processing collected images to geographic data, e.i. ortho images and 3D point clouds and then how those products can be transformed into forest data and the current use in forestry.
Wednesday 19th of September, 13 – 16 pm. Stereo-photogrammetry, production of ortho image mosaics and 3D point clouds.
Wednesday 26th of September, 13 – 16 pm. Estimation of forest variables and use of drone in forestry.
Applications to the course should be sent to firstname.lastname@example.org latest on September 18th. The amount of students on the course is limited and students how have taken the first part have priority.
The course aim is to provide students with knowledge how to conduct drone based image acquisition and forest inventory. An introduction will be given on drone systems and regulations as well as practical knowledge on how to operate drones to acquire images for mapping. The first part will focus on getting the images. The second part of the course, in the autumn, will be on data processing to get ortho-mosaics, 3D models and extract forest information.
Wednesday 23th of May, 13 – 16 p.m. Drone and camera systems and regulations. In the Ljungberg lab.
Wednesday 30th of May, 12.30 – 17 p.m. Field trip to fly drones and acquire images.
Applications to the course should be sent to email@example.com latest on May 21st. The amount of students on the course is limited.
Carl Jansson presenterar sitt examensarbete med titel ”Identifiering av fullskiktade bestånd med stereomatchande flygbilder och laserskanning”. Carl har undersökt om det går att skilja fullskiktade blädade bestånd från enskiktade bestånd skötta med trakthyggesbruk med hjälp av fjärranalys och metoder som går att automatisera och använda för stora områden.
Presentationen hålls i Årsringen på plan 1 kl 14.00-14.30. Mats Nilsson är examinator och Eva Lindberg är handledare.
We will take a break in the open lab Wednesdays. We will announce here on the website when we have open again. Mean-while you can have a look at the updated list of equipment and softwares. We will shortly provide more tutorials/instructions.
We now have “open lab” every Wednesday afternoon (13.00-16.00). At this time you can explore remote sensing data, use the lab’s sensors to collect data or follow one of our tutorials to learn how to work with remote sensing data. The tutorials is an ongoing work and will be published on the website as soon as they are ready for you. You can also ask questions to the lab’s forest remote sensing experts.
We also have a HTC Vive (Virtual Reality) that can be used to explore point clouds from mobile laser scanning, terresterial laser scanning and drone data.
Here are some quick results from the drone camera tests we did two days ago. We flow four different cameras under similar conditions. The data set should ideally be used buy a student to do some project and a deeper evaluation of the cameras. But as we know that many in the forest industri are thinking about the difference between the Dji Phantom 4pro camera with a global shutter and a larger sensor compared to the Dji Mavic pro which is smaller, cheeper and has a, for photogrammetry, poorer camera.
The drones (and cameras) was flown in the evening with low sun behind thin clouds, generating no shadows but also not bright light. This lighting condition should push the cameras a bit and at the same time give less problem with bright sunlit tree crowns against a dark shadowed ground.
A orthophoto was made in Pix4D cloud service using default settings. Here are the results:
The Phantom 4 pro camera (on the left) has a higher dynamic range in the spectral resolution and is also less over exposed on the road compared with the Mavic pro (right image).
For the final test the Sony a5100 and the Parrot sequoia needs to be added and the images needs to be compared in more depth as well as their performance when generating point clouds.
We decided to take our drones to the forest and test some cameras today. We flow two 3DR Solo; one with Parrot Sequoia and one with a new Escadrone SoloMapper (Sony QX1 camera) and two DJI drones; one Phantom 4 pro and one Mavic pro. This was done because we had a great opportunity to acquire data from four forest patches that has just been scanned with a good terrestrial laser scanner. The scans was multi-scans with 16 scans in a 8 x 2 grid. The point cloud from the laser could be used for evaluating the point clouds generated from the drone cameras.
We tried to fly slow (5 m/s) and with lots of overlap (90/90). Sadly we ran in to trouble with the new SoloMapper, it did not acquire more then 15 images and then the system stopped. This was the first time ever we tested the camera system on the drone so it was probably a installation error of ours. We switch camera to a Sony a5100 that we have used before but which doesn’t have a gimbal, so we got good quality images.
This data set, with four different cameras, would be perfect for some student to use for a project or thesis.