uav-photogrammetry from an enduser's perspective … · manual definition of approximate...

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

� Transformation of scene geometry using identical scene points (local system � target system)

� e.g. GPS points

7-PARAMETER-TRANSFORMATION OF OBJECTPOINTS


� e.g. GPS points

� Indirectly via building geometry



� 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)


Microdrone, Video(!)

�140 frames used

�No control information available

EXAMPLE FOR BAD INNER BLOCK ACCURACY


�140 frames used


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


�140 frames used


�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?

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