Hi
we are working in a university project aimed to estimate some statistics on errors comparison between a Photoscan Pro and a laser scanner survey of the same building.
We have collected more than 160 images captured from ground and by UAV including EXIF data with GPS tag (standard gps receiver).
We have made a very dense laser survey and, using a total station (teodolite) , we have also collected more than 100 GCPs around the building and ground.
The 100 GCP are all referred to one Cartesian referring system (one of the 4 different positions of total station during acquisitions) and the laser data are now referred to this system and all is ready to be compared to the Photoscan Pro model
Now we are looking for the best way to align images and georeferring these.
The tutorial you published, uses camera positions stored in a file and GCPs to optimize the model.
In our project, we don’t have a similar file and we have only poor information of camera positions (gps).
We could image to follow two ways:
1)Align images without EXIF import
2) Build geometry as cloud point
3)Detect 10-15 GCPs on images
4)Import the GCPs coordinates list
5)Optimize (for camera position and point cloud reprojection)
6) Make some statistics measuring rms with the remaining GCPs unused
but in this case the referring system, adopted by PS during align, is different from the coordinate system I import
The second way could be
3) , 4) , 1)
so the imported system and that used from PS to align should be ugual….but I didn’t find any evidence about this procedure inside you user guide.
Could you please indicate us the correct way to obtain the best referred model related on very precise GCPs acquired by the total station?
Thanks
paolo