uav-photogrammetry from an enduser's perspective … · manual definition of approximate...
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UAV-PHOTOGRAMMETRY FROM AN ENDUSER'S PERSPECTIVE
Workshop at KIT, Germany, Sept. 2012: “Challenges and opportunities of new unmanned airborne systems UAS”
MARKUS GERKE
Supported by Ali Sadeghi Naeini, GFM diploma student 2012 ([email protected])
� Part I: Software comparison: UAV-Photogrammetry from the enduser‘s perspective
� LPS
� Pix4D
� 123D catch
� Part II: The role and importance of geometric control information
CONTENTS
Part I: Software comparison
� Image orientation
� Terrainmodel (DTM) / Surfacemodel (DSM)
� Ortho image
� (topographic) map
� 3D building model
� Animations, virtual flights
TYPICAL „PRODUCTS“ DESIRED BY THE ENDUSER
� User friendly
� Automatic processing to a large extent
� Fast processing
� Inexpensive
� High quality (e.g. in terms of accuracy)
� Results are easy usable in other software packages
SOFTWARE REQUIREMENTS
� LPS (Leica Photogrammetry Suite)
� Professional photogrammetric software (Digital Photogr. Workstation)
� Image orientation, image matching (DSM/DTM filtering), ortho image, (3D) plotting
� Pix4D
� Specialised on UAV image data, webservice (or desktop version)
� Image orientation, Image matching (DOM), (true) ortho image
� 123D catch (Autodesk)
� General scene reconstruction from unordered image sets, webservice
� No direct „Geo“-relation
� Integration into ImageModeler (3D-Image-Based Modeling)
SOFTWARE PACKAGES TESTEDDRIVEN MAINLY BY AVAILABILTY
� UAV: Aibotix X6 (Hexacopter), 2kg max. payload
� Camera: Olympus EP-2
� Flight ITC parking, altitude 70m above ground
� image scale 1:8800, GSD=5cm
TESTAREA AND -DATA
� 71 images used, ca. 8-fold / 3- fold –overlap, max. base ca. 60m
� � theoretic accuracy in X,Y,Z approx. stdev=3cm
� 5 Control points measured with GPS (stddev 2-3 cm)
� Only limited check of absolute accuracy, because GPS accuracy ≈triangulation accuracy
TESTAREA AND -DATA
TESTAREA AND -DATA
Semi automatic process:
�Manual definition of approximate values (for image orientations)
�Automatic tie point matching
�Control and check points manual definable in a relatively comfortable GUI
Results:
�Residuals in object space (check points): ca. RMSE=8cm in X,Y,Z
� A bit worse than expected
LPS
DSM DTM
LPSCLASSICAL MATCHING
Ortho, observations:
�DSM: Clearly visible inter/extrapolation effects because of sparse matching
�Height accuracy in well textured parts ~5cm
�DTM still contains objects
�Ortho image geometry: error max 3pix (ground)
LPSCLASSICAL MATCHING
DSM with dense matching approach. One stereopair took 45min(!) processing � not acceptable for practical applications, but better results
LPSADVANCED MATCHING
Stereo Analyst (3D Plotting)
LPS
Overview:
Pro:
�Accuracy of triangulation only a bit worse than expected
�„topographic“ processing workflow fully supported, incl. stereoplotting
�Export of vector data (GIS, 3D)
Con:
�Approximate values for EO parameters need to be quite accurate
�DSM bad (sparse matching) or too long processing (dense matching)
�DTM (and thus ortho) only of limited use*
�Relatively expensive
*) although one must admit that this area (parked cars, small ground area remaining) is quite challening for any filtering technique.
LPS
Semi automatic process:
�Manual definition of approximate values, but not entirely necessary
�Further process fully automatic in „cloud“. Waiting time for this project: 20min. Alternative: Desktop version available
�Controll points to be measured in GUI (no check points possible)
Results:
RMSE at control points < 1cm
Comparison of slant distance between independent check points: average dist: < 1cm, stddev. 1cm
�much better than GPS, thus no better specification possible
�very good inner block accuracy
PIX4D
DSM. Hightaccuracy (good texture) ~5cm (like LPS), details well represented
PIX4D
(True) ortho image
PIX4D
Googlemaps/-earth
PIX4D
Overview:
Pro:
�Inner block accuracy at least better than GPS accuracy
�DSM and ortho image of good quality
�You only pay what you need (given broadband internet connection): eg 240€ for this project, 3 free trials
�Geotags for images do not need to be complete
�Export of EO/camera parameters for several photogr. software packages
Con:
�No DSM filtering, thus no DTM!
�Further processing, e.g. 3D plotting only possible in external software
PIX4D
� Full automatic process:
� Upload of images („Cloud“)
� After some time (here 20min): download of camera positions, 3D point cloud, 3D meshing
� If necessary manual measurement of tie points
� Only local coordinate possible, but
� Definition of scale through one known distance in object space
� Definition of local coord. system (e.g. main directions of buildings)
� Export to ImageModeler, Meshlab (OBJ), Sketchup (�collada) etc.
� Animations possible, see: http://youtu.be/-KLji0112Tg
123D CATCH
Results:
�No statistics available, therefore here only comparsion to known distances between GPS points: mean difference < 1cm, stddev. 3cm
�again in the order of GPS: no better specifiaction possible
No standard products (DSM, DGM, ortho), but 3D modeling. Interactive using images (ImageModeler), or modeling with the mesh (e.g. Sketchup)
123D CATCH
3D mesh with texture
123D CATCH
Image-Based-Modeling in ImageModeler (Autodesk)
123D CATCH
Overview:
Pro:
�Inner block accuracy at least better than GPS accuracy
�No costs apply
�Fast and easy 3D-animations
�Export in 3D modeling software
Con:
�No use of control point information possible directly (see also part II)!
�No „Geo“-products
123D CATCH
�Fast results (eg only for visualisation, image-based-modeling in ImageModeler): 123D catch ideal
�For „Geo“ applications: Pix4d ideal (but no DTM computation!)
�LPS not (yet?) optimized for this kind of data, but: direct 3D plotting possible
Similar interesting programms might be
AgiSoft
Photoscan
Acute 3D (commercial version of 123D catch)
CONCLUSIONS PART I
Part II: The role and importance of geometric control information
The user often wants to combine UAV-images with existing (geo) information � need for georeferencing.
�Without geometric control information: euclidean scene reconstruction, but unknown scale, translation and rotation with respect to target coordinate system (i.e. realisation of a local euclidean coordinate system)
�Possible solutions:
� Direct use of control point information in bundle block adjustment. „Traditional“ photogrammetric approach, like implemented in LPS or Pix4D� ideal solution (given that control information is accurate enough)
� 7 parameter transformation (3D similarity) of object points in local system (and if needed of camera orientations). Implemented e.g. In Autodesk ImageModeler
SCENE GEOMETRY
The user often wants to combine UAV-images with existing (geo) information � need for georeferencing.
�Without geometric control information: euclidean scene reconstruction, but unknown scale, translation and rotation with respect to target coordinate system (i.e. realisation of a local euclidean coordinate system)
�Possible solutions:
� Direct use of control point information in bundle block adjustment. „Traditional“ photogrammetric approach, like implemented in LPS or Pix4D� ideal solution (given that control information is accurate enough)
� 7 parameter transformation (3D similarity) of object points in local system (and if needed of camera orientations). Implemented e.g. In Autodesk ImageModeler
SCENE GEOMETRY
� Transformation of scene geometry using identical scene points (local system � target system)
� e.g. GPS points
7-PARAMETER-TRANSFORMATION OF OBJECTPOINTS
� Transformation of scene geometry using identical scene points (local system � target system)
� e.g. GPS points
� Indirectly via building geometry
7-PARAMETER-TRANSFORMATION OF OBJECTPOINTS
7-PARAMETER-TRANSFORMATION OF OBJECTPOINTS
� Transformation of scene geometry using identical scene points (local system � target system)
� e.g. GPS points
� Indirectly via building geometry
Attention: inner accuracy of image block might be critical for geometry �7 parameter transform does not change the inner geometry (rigid body transform)
7-PARAMETER-TRANSFORMATION OF OBJECTPOINTS
Microdrone, Video(!)
�140 frames used
�No control information available
EXAMPLE FOR BAD INNER BLOCK ACCURACY
Microdrone, Video(!)
�140 frames used
�No control information available
EXAMPLE FOR BAD INNER BLOCK ACCURACY
After free network adjustment: Tie points in object spaceOrange lines represent gutter/eave lines of the building which should be
horizonal, but are not! � bad inner block accuracy
On Literature: e.g. Use of existing DSM or 3D city model
Here: � use of “scene constraints”
WHAT IF WE DO NOT HAVE CONTROL POINTS?
� Horizontal/vertical structure at buildings
� “soft constraints” (observations) in least squares bundle adjustment
� Useful to achieve correct scene geometry
� Less (oder none) control points needed
Gerke, M. (2011) Using horizontal and vertical building structure to constrain indirect
sensor orientation. In: ISPRS Journal of Photogrammetry and Remote Sensing, 66
(2011),3, pp. 307-316
SCENE CONSTRAINTS IN INDIRECT SENSOR ORIENTATION
Microdrone, Video(!)
�140 frames used
�No control information available
�But: use of 5 horizonal lines � eave and gutter lines are horizontal after adjustment
SCENE CONSTRAINTS IN INDIRECT SENSOR ORIENTATION
Dense image matching
�pointcloud
DERIVED PRODUCTS ARE OF MUCH BETTER QUALITY…
Dense image matching
�pointcloud
�DSM, DTM, ortho image
DERIVED PRODUCTS ARE OF MUCH BETTER QUALITY…
� Inner block geometry important � check always!
� Depending on application, image configuration and quality a 7 parameter transform might not be good enough
� Problem in current (free) software without „geo“ relation not fully addressed
CONCLUSIONS PART II
Thank you for your attention!
Questions?