ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
GENERATION AND WEIGHTING OF 3D POINT CORRESPONDENCES FOR
IMPROVED REGISTRATION OF RGB-D DATA
K. Khoshelham a,*, D. R. Dos Santos b, G. Vosselman a
a
Faculty of Geo-Information Science and Earth Observation, University of Twente, Netherlands - (k.khoshelham,
george.vosselman)@utwente.nl
b
Federal University of Paraná, Curitiba, Brazil - danielsantos@ufpr.br
Commission III/WG III-2
KEY WORDS: Alignment, Indoor Mapping, Kinect, Loop Closing, Point Cloud, RGB-D, SLAM.
ABSTRACT:
Registration of RGB-D data using visual features is often influenced by errors in the transformation of visual features to 3D space as
well as the random error of individual 3D points. In a long sequence, these errors accumulate and lead to inaccurate and deformed
point clouds, particularly in situations where loop closing is not feasible. We present an epipolar search method for accurate
transformation of the keypoints from 2D to 3D space, and define weights for the 3D points based on the theoretical random error of
depth measurements. Our results show that the epipolar search method results in more accurate 3D correspondences. We also
demonstrate that weighting the 3D points improves the accuracy of sensor pose estimates along the trajectory.
1. INTRODUCTION
Since their recent introduction to the market, RGB-D cameras,
such as the Kinect (Microsoft, 2010), have gained a lot of
popularity for indoor mapping, modelling and navigation. The
Kinect sensor captures depth and colour images at a rate of
20~30 frames per second, which can be combined into a
coloured point cloud, also referred to as RGB-D data.
Compared to laser scanning, Kinect RGB-D data have lower
accuracy and resolution (Khoshelham, 2011). However, the
high data acquisition rate and the great flexibility of the Kinect
make it an attractive sensor for mapping and modelling indoor
environments.
A primary step in mapping by RGB-D data is the registration of
successive frames. The common approach is based on visual
features, i.e. point correspondences extracted from the colour
images by keypoint extraction and matching methods such as
SIFT (Lowe, 2004) and SURF (Bay et al., 2008). These point
correspondences are transformed to 3D space by using the
depth data, and are then used to estimate the rotation and
translation between every pair of frames.
The pairwise registration is prone to error due to the random
error of individual points but also the transformation from the
colour space to the depth space. In a long sequence, the
pairwise registration errors accumulate and lead to deformation
in the resulting point cloud. To cope with registration errors,
loop closing has been used (May et al., 2009; Du et al., 2011;
Endres et al., 2012; Henry et al., 2012). A loop in the trajectory
of the sensor can be detected when the sensor returns to a scene
that is previously observed. Loop closing is essentially a global
adjustment of the sensor pose (position and rotation)
simultaneously for all frames in a sequence.
Loop closing is not always feasible, for example when mapping
a long narrow corridor, or when the two frames at the closing
do not have sufficient overlap or reliable keypoint matches. In
such situations, improvement of the pairwise registrations is
important as it can reduce the error and deformations in the
final point cloud.
In this paper, we look into two sources of error in pairwise
registration based on visual features: the error in the
transformation from the RGB space to the depth space, and the
random error of individual points in the 3D space. We present a
method for accurate transformation of point features from the
RGB space to the depth space, and propose a weighting scheme
to adjust the contribution of the 3D point correspondences in
the estimation of the registration parameters. Our experiments
show the role of relative orientation in the accuracy of the 3D
point correspondences. We also demonstrate that weighting
point correspondences based on their theoretical random error
improves the registration accuracy.
The paper proceeds with a review of related literature in Section
2. In Section 3, the methods for the generation and weighting of
3D point correspondences are described. Section 4 describes the
experiments and results of registration using weighted point
correspondences. The paper concludes with final remarks in
Section 5.
2. RELATED WORK
The popular approach to registering point clouds is the iterative
closest point (ICP) algorithm (Besl and McKay, 1992; Chen
and Medioni, 1992). Izadi et al. (2011) showed real-time
registration of Kinect depth images using a GPU
implementation of the ICP algorithm. The method of Fioraio
and Konolige (2011) was also based on ICP, but could integrate
features from the colour image.
Since ICP is a fine registration method requiring a close
approximation of the registration parameters, it has been often
used to refine an initial coarse registration. In the work of Henry
et al. (2010), the initial registration parameters were estimated
* Corresponding author.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-127-2013
127
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
from SIFT key points (Lowe, 2004) extracted from and matched
across the colour images, where outliers were removed using
RANSAC (Fischler and Bolles, 1981). Du et al. (2011)
followed a similar approach but allowed user interaction. The
RGB-D SLAM method (Engelhard et al., 2011; Endres et al.,
2012) and the method of Bachrach et al., (2012) were both
based on the idea of initial registration using visual feature
points, although they used different feature extraction operators.
Dryanovski et al. (2012) performed the initial registration based
on edge features extracted from the colour images. Steinbrucker
et al. (2011) adopted an energy minimization approach to
registering RGB-D data.
For loop closing several methods have been used. Graph-based
optimization methods (Olson et al., 2006; Grisetti et al., 2007;
Kummerle et al., 2011) represent the poses and their constraints
as nodes and edges of a graph, and apply an optimization
method such as gradient decent to minimize the error. Sparse
bundle adjustment (Lourakis and Argyros, 2009) involves leastsquares (re-)estimation of pose parameters by minimizing the
re-projection error in the image space.
Other types of correspondences, such as planes (Brenner and
Dold, 2007; Khoshelham and Gorte, 2009; Khoshelham, 2010),
point-planes (Sande et al., 2010; Grant et al., 2012) and lines
(Bucksch and Khoshelham, 2013; dos Santos et al., 2013), have
been used for registering laser scanner point clouds. Taguchi et
al. (2012) combined points and planes for the registration of
RGB-D data. Dou et al. (2013) combined planes with visual
features in both pairwise registration and global adjustment. A
comparison of RANSAC and Hough transform for plane
extraction and mapping using RGB-D data is presented by Nasir
et al. (2012).
3. GENERATION AND WEIGHTING OF 3D POINT
CORRESPONDENCES
In this paper, we follow the concept of initial pairwise
registration using point features extracted from the colour
images. We focus on two aspects in this approach:
transformation of the colour image features to the depth image
for the generation of 3D point correspondences, and weighting
of the 3D point pairs based on the theoretical random error of
individual points.
3.1 3D point correspondences from 2D keypoints
We use SURF (Bay et al., 2008) to extract and match keypoints
in successive colour images as it is considerably faster than
similar algorithms. The keypoints are defined in the 2D
coordinate system of the colour image. For the estimation of the
pairwise registration parameters the 2D points should be
transformed to 3D space by using the depth data. We define the
3D coordinate system of the point cloud with its origin at the
centre of the infrared camera, the Z axis perpendicular to the
image plane, the X axis perpendicular to the Z axis in the
direction of the baseline between the infrared camera centre and
the laser projector, and the Y axis orthogonal to X and Z
making a right handed coordinate system.
To generate 3D correspondences from the 2D keypoints, in
some previous works it has been assumed that a shift of the
depth image pixels (applied within the driver) is sufficient to
align the depth image with the colour image (Engelhard et al.,
2011; Endres et al., 2012; Henry et al., 2012). As we will show,
there are cases where the shift between the coordinates of
conjugate points in the colour image and the depth image has a
large variance, even when the image coordinates are corrected
for lens distortions.
A more proper way to transform the coordinates from the colour
image to the depth image is by using the relative orientation
parameters (three rotations and three translations – different
from photogrammetric relative orientation which involves five
parameters) between the two cameras. This of course requires
that the relative orientation parameters are estimated in a
previous calibration procedure. For the estimation of relative
orientation parameters stereo calibration with a calibration grid
has been used (Khoshelham and Elberink, 2012). This method
provides relative orientation parameters but with relatively low
accuracy due to the short length of the baseline between the two
cameras in proportion to the distance to the calibration grid.
Another approach is by using a 3D calibration field with
markers that can be measured in the depth image as well as in
the colour image. By measuring the markers in the depth image
the 3D coordinates of the points are obtained in the infrared
camera coordinate system. Using the 3D coordinates in the
infrared frame and the corresponding 2D coordinates in the
RGB frame the transformation between the two frames can be
obtained by a least-squares space resection procedure.
The estimated orientation parameters allow the transformation
of 3D points to the colour image (back projection), whereas we
need to transform the 2D keypoints to the 3D space. This is an
ill-posed problem. To overcome that, we make use of the
epipolar geometry in the following procedure:
Given a keypoint in the RGB frame:
1. calculate the epipolar line in the depth frame using the
relative orientation parameters;
2. define a search band along the epipolar line using the
minimum and maximum of the range of depth values
(0.5 m and 5 m respectively);
For all pixels within the search band:
1. calculate 3D coordinates and re-project the
resulting 3D point back to the RGB frame;
2. calculate and store the distance between the reprojected point and the original keypoint;
Return the 3D point whose re-projection has the smallest
distance to the keypoint.
Note that interior orientation parameters (including lens
distortion) are used in both frames to transform back and forth
between pixel coordinates and image coordinates. When the
distance between the keypoint and the nearest re-projected point
is larger than a threshold (e.g. 2 pixels) the keypoint is flagged
as not having a valid 3D correspondence. Figure 1 illustrates the
procedure in a test scene.
Figure 1. Finding 3D points in the depth image (right)
corresponding to 2D keypoints in the colour image (left) by
searching along epipolar lines (red bands).
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-127-2013
128
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
3.2 Definition of weights
Pairwise registration involves the estimation of a rotation matrix
R and a translation vector t between two sets of corresponding
points, which minimize the error:
E j wi X i , j 1 RX i , j t
n
i 1
(1)
where Xi,j-1 and Xi,j are the 3D coordinates of point i in frames
j-1 and j respectively, and wi is the weight associated to the
point pair i. Since points in the Kinect point clouds do not have
a uniform precision (Khoshelham, 2011), it makes perfect sense
to weight the points according to their random errors.
As Kinect depth images are captured typically at a frame rate of
20 to 30 fps, resulting in small rotation and translation
parameters between successive frames, we can approximate our
observation equations with vi = Xi,j-1 – Xi,j , for which the
weight can be defined inversely proportional to the variance of
the observation:
k
k
wi 2 2
(2)
vi Xi , j 1 X2 i , j
where σ2X is the variance of point X and k is an arbitrary
constant.
We define the weights for every pair of corresponding points
based on the theoretical random error of their depth values (Z)
only. This is because weighting based on the error of X, Y
coordinates would reduce the contribution of the points with
increasing distance from the centre of the point cloud, which is
counter-intuitive as off-centre points are expected to play a
more important role in the correct alignment of two surfaces. It
has been shown that the variance of the depth σ2Z has the
following relation with the variance of the measured disparity
σ2d (Khoshelham and Elberink, 2012):
Z2 c12 d2 Z 4
kc12 d2
Z i4, j 1 Z i4, j
Figure 3 (a) shows first the difference between the colour image
coordinates and the depth image coordinates (both corrected for
lens distortion using the model of Brown (1971)) of the markers
to test whether the transformation is only a shift. Clearly, there
is a large variance in the shift between the two sets of
coordinates. Figure 3(b) shows the discrepancies between the
measured and the transformed coordinates of the keypoints,
where the relative orientation parameters from the stereo
calibration are used for the transformation. Figure 3(c) shows
the discrepancies between the measured and the transformed
coordinates of the keypoints, where the relative orientation
parameters from the space resection method are used. It can be
seen in Figure 3(c) that the transformed points have a variance
of about 1 pixel. This shows that transforming the points by the
epipolar search method and using the relative orientation
parameters from the space resection is more accurate and
reliable than the other methods.
(3)
where c1 is a depth calibration parameter. This gives us the
following equation for the weight of a point pair:
wi
The transformation was done using two sets of relative
orientation parameters. The first set was obtained by a standard
stereo calibration procedure using a calibration grid. The second
set was obtained by the space resection method using a 3D
calibration field similar to the scene shown in Figure 2. The
discrepancies between the manually measured coordinates of
the markers in the depth image, and the transformed coordinates
obtained by each of the two sets of relative orientation
parameters provide an indication of the error in transforming
the keypoints from the 2D space to the 3D space.
Figure 2. Manually measured markers in the disparity (left) and
colour image (right).
(4)
3.3 Pairwise registration
Once the corresponding 3D points and their associated weights
are obtained the point clouds of two successive frames can be
registered. The common approach, which is also used here, is to
combine the least-squares estimation method with RANSAC to
eliminate the outliers (Hartley and Zisserman, 2003). To speed
up the registration we use Horn’s closed-form solution (Horn,
1987) to estimate the registration parameters for each random
sample within RANSAC. Once the inliers are identified, a final
iterative least-squares estimation using weighted inlier points is
performed to obtain the registration parameters.
(a)
(b)
4. EXPERIMENTS
To show the effect of relative orientation on the transformation
of keypoints from the RGB space to the depth space we made a
test scene with markers that could be measured manually in
both the depth image and the colour image. The markers were
captured and measured in seven pairs of images. Figure 2 shows
one of the seven pairs. The coordinates of the markers were then
transformed from the colour image to the depth image using the
epipolar search method as described in Section 3.1.
(c)
Figure 3. Discrepancies between the manually measured and
transformed coordinates of the markers using only a shift (a),
using parameters from stereo calibration (b) and using
parameters from space resection (c).
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-127-2013
129
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
To study the effect of weighting 3D point correspondences in
pairwise registration a set of six RGB-D sequences from an
office environment was acquired. Since obtaining ground truth
trajectories was difficult, the sequences were acquired such that
the first and the last frame of each sequence had sufficient
overlap and could be registered to form a closed loop. This
allowed the calculation of the closing error for each trajectory
based on the following equation:
v
TR H12 Hnn1H1n
0 1
Average closing
distance [cm]
Average closing
angle [deg]
without weight
6.42
6.32
with weight
3.80
4.74
Registration
Table 1. Average closing errors for registrations with and
without weight.
(5)
where Hij denotes the transformation from frame i to frame j,
and Δ is a residual transformation matrix containing a closing
translation vector v and a closing rotation matrix δR. From these
we calculated two error metrics to evaluate the accuracy of each
trajectory: a closing distance from v and a closing angle as the
sum of (absolute) rotation angles in δR.
Figure 4 shows the closing distances and closing angles for the
six sequences after the pairwise registration with and without
weights. The sequences were sorted in order of increasing
length, and the horizontal axes show sequence length. It can be
seen that both the closing distances and closing angles are
improved as a result of using weights in pairwise registrations.
Table 1 shows the average closing distance and closing angle
over all sequences registered with and without weights.
Figure 5. Trajectory obtained by weighted registration of an
RGB-D sequence (in blue) compared with the trajectory
obtained by registration without weights (in red) and one
obtained by global adjustment (in black).
Figure 5 compares for one of the sequences the trajectory
obtained by weighted registration (blue curve) with that
obtained by registration without weights (red curve). The black
curve is the closed loop obtained by a global adjustment of the
sensor poses. It can be seen that the trajectory from the
weighted registration follows more closely the globally adjusted
trajectory. Example point clouds of an office environment
obtained by the weighted registration of RGB-D sequences are
shown in Figure 6.
(a)
(b)
Figure 4. Closing distance (a) and closing angle (b) for six
RGB-D sequences registered with and without weights.
Figure 6. Example point clouds of an office environment
obtained by weighted registration of RGB-D sequences.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-127-2013
130
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
5. CONCLUSIONS
When registering long RGB-D sequences, pairwise registration
errors accumulate and lead to inaccurate and deformed point
clouds, particularly in situations where loop closing is not
feasible. We showed that accurate transformation of keypoints
from the RGB space to the depth space using an epipolar search
method results in more accurate 3D point correspondences. We
also showed that assigning weights based on the theoretical
random error of the depth measurements improves the accuracy
of pairwise registration and sensor pose estimates along the
trajectory.
Using weighted observations in pairwise registration allows the
estimation of covariance matrices for the estimated pose
vectors. These can be used to weight pose vectors in the global
adjustment, and further improve the sensor pose estimates in a
closed loop.
A drawback of registration by using visual features is the
influence of synchronization errors between the RGB camera
shutter and the IR camera shutter on the transformation of
keypoints to the 3D space. This emphasises the importance of a
fine registration step using point- and plane correspondences
extracted from the depth images to generate accurate point
clouds from RGB-D data.
REFERENCES
Bachrach, A., Prentice, S., He, R., Henry, P., Huang, A.S.,
Krainin, M., Maturana, D., Fox, D., Roy, N., 2012.
Estimation, planning, and mapping for autonomous flight
using an RGB-D camera in GPS-denied environments. The
International Journal of Robotics Research 31(11), 13201343.
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V., 2008. SURF:
Speeded Up Robust Features. Computer Vision and Image
Understanding 110(3), 346-359.
Besl, P.J., McKay, N.D., 1992. A method for registration of 3-D
shapes. IEEE Transactions on Pattern Analysis and Machine
Intelligence 14(2), 239-256.
Brenner, C., Dold, C., 2007. Automatic relative orientation of
terrestrial laser scans using planar structures and angle
constraints, ISPRS Workshop on Laser Scanning 2007 and
SilviLaser 2007, Espoo, Finland, pp. 84-89.
Brown, D.C., 1971. Close-range camera calibration.
Photogrammetric Engineering 37(8), 855-866.
Bucksch, A., Khoshelham, K., 2013. Localized Registration of
Point Clouds of Botanic Trees. IEEE Geoscience and Remote
Sensing Letters 10(3), 631-635.
Chen, Y., Medioni, G., 1992. Object modeling by registration
of multiple range images. Image and Vision Computing
10(3), 145-155.
dos Santos, D.R., Dal Poz, A.P., Khoshelham, K., 2013.
Indirect Georeferencing of Terrestrial Laser Scanning Data
using Control Lines. The Photogrammetric Record 28(143),
276-292.
Dou, M., Guan, L., Frahm, J.-M., Fuchs, H., 2013. Exploring
high-level plane primitives for indoor 3d reconstruction with
a hand-held RGB-D camera. Proceedings of the 11th
international conference on Computer Vision - Volume 2,
Daejeon, Korea.
Dryanovski, I., Jaramillo, C., Jizhong, X., 2012. Incremental
registration of RGB-D images, IEEE International
Conference on Robotics and Automation (ICRA), St. Paul,
Minnesota, pp. 1685-1690.
Du, H., Henry, P., Ren, X., Cheng, M., Goldman, D.B., Seitz,
S.M., Fox, D., 2011. Interactive 3D modeling of indoor
environments with a consumer depth camera. Proceedings of
the 13th international conference on Ubiquitous computing,
Beijing, China.
Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D.,
Burgard, W., 2012. An evaluation of the RGB-D SLAM
system, IEEE International Conference on Robotics and
Automation (ICRA), St. Paul, Minnesota, pp. 1691-1696.
Engelhard, N., Endres, F., Hess, J., Sturm, J., Burgard, W.,
2011. Realtime 3D visual SLAM with a hand-held RGB-D
camera. RGB-D Workshop on 3D Perception in Robotics at
the European Robotics Forum, Vasteras, Sweden.
Fioraio, N., Konolige, K., 2011. Realtime visual and point
cloud slam. RGB-D Workshop on Advanced Reasoning with
Depth Cameras at Robotics: Science and Systems Conf.
(RSS), University of Southern California.
Fischler, M.A., Bolles, R.C., 1981. Random sample consensus:
a paradigm for model fitting with applications to image
analysis and automated cartography. Communications of the
ACM 24(6), 381-395.
Grant, D., Bethel, J., Crawford, M., 2012. Point-to-plane
registration of terrestrial laser scans. ISPRS Journal of
Photogrammetry and Remote Sensing 72(0), 16-26.
Grisetti, G., Stachniss, C., Grzonka, S., Burgard, W., 2007. A
tree parameterization for efficiently computing maximum
likelihood maps using gradient descent. Proc. of Robotics:
Science and Systems (RSS), Atlanta, GA, USA.
Hartley, R., Zisserman, A., 2003. Multiple view geometry in
computer vision, 2nd edition. Cambridge University Press,
Cambridge, UK.
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D., 2010.
RGB-D Mapping: Using Depth Cameras for Dense 3D
Modeling of Indoor Environments. Proc. of International
Symposium on Experimental Robotics (ISER), Delhi, India.
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D., 2012.
RGB-D mapping: Using Kinect-style depth cameras for dense
3D modeling of indoor environments. International Journal
of Robotics Research 31(5), 647-663.
Horn, B.K.P., 1987. Closed-form solution of absolute
orientation using unit quaternions. Journal of the Optical
Society of America a-Optics Image Science and Vision 4(4),
629-642.
Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R.,
Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A.,
Fitzgibbon, A., 2011. KinectFusion: Real-time 3D
Reconstruction and Interaction Using a Moving Depth
Camera. ACM Symposium on User Interface Software and
Technology, Santa Barbara, California.
Khoshelham, K., 2010. Automated Localization of a Laser
Scanner in Indoor Environments Using Planar Objects.
International Conference on Indoor Positioning and Indoor
Navigation (IPIN), Zürich, Switzerland.
Khoshelham, K., 2011. Accuracy analysis of kinect depth data.
ISPRS workshop laser scanning 2011, Calgary, Canada.
Khoshelham, K., Elberink, S.O., 2012. Accuracy and
Resolution of Kinect Depth Data for Indoor Mapping
Applications. Sensors 12(2), 1437-1454.
Khoshelham, K., Gorte, B.G., 2009. Registering point clouds of
polyhedral buildings to 2D maps. Proceedings of the 3rd
ISPRS International Workshop 3D-ARCH 2009: "3D Virtual
Reconstruction and Visualization of Complex Architectures",
Trento, Italy.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-127-2013
131
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard,
W., 2011. g2o: A general framework for graph optimization,
IEEE International Conference on Robotics and Automation
(ICRA), Shanghai, China, pp. 3607-3613.
Lourakis, M.I.A., Argyros, A.A., 2009. SBA: A Software
Package for Generic Sparse Bundle Adjustment. ACM
Transactions on Mathematical Software 36(1), 1-30.
Lowe, D.G., 2004. Distinctive Image Features from ScaleInvariant Keypoints. International Journal of Computer
Vision 60(2), 91-110.
May, S., Droeschel, D., Holz, D., Fuchs, S., Malis, E., Nüchter,
A., Hertzberg, J., 2009. Three-dimensional mapping with
time-of-flight cameras. Journal of Field Robotics 26(11-12),
934-965.
Microsoft, 2010. Kinect for Windows sensor components and
specifications.
http://msdn.microsoft.com/enus/library/jj131033.aspx (26 June 2013).
Nasir, A.K., Hille, C., Roth, H., 2012. Plane Extraction and
Map Building Using a Kinect Equipped Mobile Robot.
IEEE/RSJ International Conference on Intelligent Robots and
Systems, IROS 2012, Workshop on Robot Motion Planning:
Online, Reactive, and in Real-time, Vilamoura, Algarve,
Portugal.
Olson, E., Leonard, J., Teller, S., 2006. Fast iterative
optimization of pose graphs with poor initial estimates. IEEE
International Conference on Robotics & Automation (ICRA),
Orlando, Florida.
Sande, C.v.d., Soudarissanane, S., Khoshelham, K., 2010.
Assessment of Relative Accuracy of AHN-2 Laser Scanning
Data Using Planar Features. Sensors 10(9), 8198-8214.
Steinbrucker, F., Sturm, J., Cremers, D., 2011. Real-time visual
odometry from dense RGB-D images, IEEE International
Conference on Computer Vision Workshops (ICCV
Workshops), Barcelona, Spain, pp. 719-722.
Taguchi, Y., Jian, Y.D., Ramalingam, S., Feng, C., 2012.
SLAM using both points and planes for hand-held 3D
sensors. . IEEE International Symposium on Mixed and
Augmented Reality, Atlanta, USA.
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-127-2013
132