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Figure2: Left: Thesetupof theDSLRandlaser rangefinderonthecarbonpanel. Right: Theschemat-
icsof thecameraand laser rangefinder setup and the ideabehindcalibration.
3. CameraSetupandCalibration
Our hardware setup consists of a standard DSLR mounted onto a rigid carbon panel next to a 1D
LRF (see Fig. 2), which we control remotly via USB and Bluetooth for easy acquisition of images
and distance measurements. We perform intrinsic calibration with a modified version of the Bouguet
toolbox and a custom target as proposed in [4]. In the remaining part of the paper we expect the
camera to be calibrated and the geometric distortions introduced by the camera and lens assembly to
be removed from the images. This enables us to infer the real world line of sight lloswith respect to
thecenterofprojectionof thecameraof any givenpixel inan imageIi as:
llos,i=K −1 · l2D,i,i, (1)
whereKdenotes the camera matrix and l2D,i,i= [x,y,1]T is the 2D position of the laser point i inIi
inhomogeneouscoordinates.
In theory, provided an intrinsic camera calibrationK and a known plane in 3D, the rotationRLRF
and translationtLRF of the laser range finder relative to the camera can be estimated using two mea-
surements only. Yet, in order to obtain a more robust estimation, we take several measurements
M = {di,Ii}N1 of distances di with corresponding images Ii. Since the application is 3D facade
reconstructionandweexpect the facademeasurements tobe takennearly fronto-parallel to the image
sensor, we restrict the extrinsic calibration sequence to a fronto-parallel movement of the target rela-
tive to the camera, ensuring that the position at which the LRF takes its measurement is well within
thecalibration target.
In a first step, we detect the target position and orientation in 3D relative to the camera’s center of
projection,aswellas the targetposition in2Dwithin the image. Wedetect the laserpointasbrightest
object on the calibration target using adaptive thresholding and then take its center of mass as the 2D
position l2D,i,i = [xi,yi,1]T of the laser point i in Ii. We then calculate the position l3D,i in 3D of a
laserpointby intersecting the lineof sight llos,i, on which the laserpoint lies,with the targetplane.
When holding the camera positions fixed and moving the calibration target plane relative to it, all
points l3D,i, i ∈ {1, . . . ,N} lie on a straight line. This line corresponds to the viewing direction
lLRF of the LRF, which we calculate by fitting a line to the measurements using singular value de-
compositiononthe3DpointsstackedtoamatrixL3D= [l3D,1, · ·· ,l3D,N]. Theright-singularvectors
obtainedbySVDcorrespondtotheorthogonaldirectionsofmaximumvariancewithinthedata. Thus,
theright-singularvectorcorrespondingtothelargestsingularvalueofL3D coincideswith theviewing
directionof theLRF,provided that theuncertaintyof theestimationof theLRF’sorigin issufficiently
smaller than the relativemovementof thecalibration target.
79
Proceedings
OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
- Title
- Proceedings
- Subtitle
- OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
- Authors
- Peter M. Roth
- Kurt Niel
- Publisher
- Verlag der Technischen Universität Graz
- Location
- Wels
- Date
- 2017
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-527-0
- Size
- 21.0 x 29.7 cm
- Pages
- 248
- Keywords
- Tagungsband
- Categories
- International
- Tagungsbände